Rent in lagos_renwed-copy 1.ipynb

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    "## Introduction:\n",
    "\n",
    "### 1.1 Context\n",
    "House hunting in Lagos can be a very frustrating process. From being shown houses with ridiculously small spaces to being asked to pay outrageous prices for those spaces, the entire process can be very time-consuming and demoralizing. The prices of houses are a major problem, especially when you don't know what to expect.\n",
    "\n",
    "This notebook explores the factors that influence rental prices in Lagos, Nigeria. Using a dataset of property features, we will build a predictive model that can estimate rental prices. This will help landlords, tenants, and real estate enthusiasts make informed decisions in the Lagos rental market.\n",
    "\n",
    "### 1.2 Data Features\n",
    "The data for this analysis was gotten from Kaggle, which was originally scrapped from property.pro.ng, a webpage that provides information for houses available for rent in Nigeria. Here is what the data looks like:\n",
    "\n",
    "* Title: Title of the property e.g. Brand New 5 Bedroom Semi-Detached Duplex\n",
    "* More Info: More Information about the property e.g. 4 BEDROOM HOUSE FOR RENT\n",
    "* City: City where the property is located e.g. Lekki\n",
    "* Neighborhood: Where the property is located e.g. Osapa London\n",
    "* Serviced: Whether the property has been serviced e.g. sockets have been installed or other renovations.\n",
    "* Newly Built: Whether the property was newly built\n",
    "* Furnished: Whether the property has been furnished\n",
    "* Bedrooms: Number of bedrooms on the property\n",
    "* Bathrooms:Number of bathrooms on the property\n",
    "* Toilets: Number of toilets on the property\n",
    "* Price: The price of the property\n",
    "\n",
    "\n",
    "### 1.3 Pre-Requsities\n",
    "For this project we would be making use of several libraries, the first set being the usual libraries, Numpy,Pandas for manipulating data and seaborn and Matplotlib for visualization. Then we would be importing several libraries from sklearn for prediction. Some of the basic ones we would be using are; RandomForest Regressor, Ridge Regressor, DecisionTreeRegressor, Lasso Regressor, etc.\n",
    "The several  model: \n",
    "  \n",
    " NB: Any model not stated in the original library importation field would be explained as we go"
   ]
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   "source": [
    "#Importing important libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# ML library\n",
    "import sklearn\n",
    "\n",
    "# Ignore warnings to keep the output clean\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
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   "id": "1c4d4a2c",
   "metadata": {},
   "source": [
    "## 2.0  Data Wrangling\n",
    "\n",
    "Here we would be looking at the following:\n",
    "* Loading the data.\n",
    "* Taking out commercail locations in the dataset\n",
    "* Dropping unneeded columns(Title and More info column)\n",
    "* Handling the numeric columns; Price, Bedrooms, Bathrooms, Toilets\n",
    "* Getting the statistics of the dataset\n",
    "* Exploring the statistics of the data.\n",
    "\n",
    "### 2.1 Loading the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1637e91",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>More Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Serviced</th>\n",
       "      <th>Newly Built</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Newly Built 4 Bedroom Semi Detached House With Bq</td>\n",
       "      <td>4 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>5,000,000/year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>4 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Agungi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Superb 4 Bedroom Semi Detached Duplex With Bq</td>\n",
       "      <td>4 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>5,000,000/year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>4 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Other Lekki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Furnished 2 Bedroom Terrace Duplex For Rent !!!</td>\n",
       "      <td>2 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>3,500,000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>3 baths</td>\n",
       "      <td>3 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Osapa London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 Bedroom Apartment For Rent</td>\n",
       "      <td>2 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>2,700,000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>3 baths</td>\n",
       "      <td>3 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Ologolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4 Bedroom Terrace Duplex For Rent</td>\n",
       "      <td>4 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>4,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>5 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Chevron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title  \\\n",
       "0  Newly Built 4 Bedroom Semi Detached House With Bq   \n",
       "1      Superb 4 Bedroom Semi Detached Duplex With Bq   \n",
       "2    Furnished 2 Bedroom Terrace Duplex For Rent !!!   \n",
       "3                       2 Bedroom Apartment For Rent   \n",
       "4                  4 Bedroom Terrace Duplex For Rent   \n",
       "\n",
       "                  More Info           Price  Serviced  Newly Built  Furnished  \\\n",
       "0  4 BEDROOM HOUSE FOR RENT  5,000,000/year         0            1          0   \n",
       "1  4 BEDROOM HOUSE FOR RENT  5,000,000/year         0            1          0   \n",
       "2  2 BEDROOM HOUSE FOR RENT       3,500,000         1            0          0   \n",
       "3  2 BEDROOM HOUSE FOR RENT       2,700,000         1            1          0   \n",
       "4  4 BEDROOM HOUSE FOR RENT       4,000,000         1            0          0   \n",
       "\n",
       "  Bedrooms Bathrooms    Toilets   City  Neighborhood  \n",
       "0   4 beds   4 baths  5 Toilets  Lekki        Agungi  \n",
       "1   4 beds   4 baths  5 Toilets  Lekki   Other Lekki  \n",
       "2   2 beds   3 baths  3 Toilets  Lekki  Osapa London  \n",
       "3   2 beds   3 baths  3 Toilets  Lekki       Ologolo  \n",
       "4   4 beds   5 baths  5 Toilets  Lekki       Chevron  "
      ]
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   ],
   "source": [
    "# Reading the data file\n",
    "df = pd.read_csv('lagos-rent_renewed.csv')\n",
    "df.head(5)"
   ]
  },
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   "cell_type": "markdown",
   "id": "98808f6f",
   "metadata": {},
   "source": [
    "### 2.2 Dropping Rows with Non-residential Addresses\n",
    "For this analysis we are more concerned with just residential buildings, so we will be dropping rows that include details for commerical buildings, church, office space, conferences, schools, and even lands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e978f20",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>More Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Serviced</th>\n",
       "      <th>Newly Built</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Newly Built 4 Bedroom Semi Detached House With Bq</td>\n",
       "      <td>4 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>5,000,000/year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>4 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Agungi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Superb 4 Bedroom Semi Detached Duplex With Bq</td>\n",
       "      <td>4 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>5,000,000/year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>4 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Other Lekki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Furnished 2 Bedroom Terrace Duplex For Rent !!!</td>\n",
       "      <td>2 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>3,500,000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>3 baths</td>\n",
       "      <td>3 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Osapa London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 Bedroom Apartment For Rent</td>\n",
       "      <td>2 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>2,700,000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>3 baths</td>\n",
       "      <td>3 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Ologolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4 Bedroom Terrace Duplex For Rent</td>\n",
       "      <td>4 BEDROOM HOUSE FOR RENT</td>\n",
       "      <td>4,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>5 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Chevron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title  \\\n",
       "0  Newly Built 4 Bedroom Semi Detached House With Bq   \n",
       "1      Superb 4 Bedroom Semi Detached Duplex With Bq   \n",
       "2    Furnished 2 Bedroom Terrace Duplex For Rent !!!   \n",
       "3                       2 Bedroom Apartment For Rent   \n",
       "4                  4 Bedroom Terrace Duplex For Rent   \n",
       "\n",
       "                  More Info           Price  Serviced  Newly Built  Furnished  \\\n",
       "0  4 BEDROOM HOUSE FOR RENT  5,000,000/year         0            1          0   \n",
       "1  4 BEDROOM HOUSE FOR RENT  5,000,000/year         0            1          0   \n",
       "2  2 BEDROOM HOUSE FOR RENT       3,500,000         1            0          0   \n",
       "3  2 BEDROOM HOUSE FOR RENT       2,700,000         1            1          0   \n",
       "4  4 BEDROOM HOUSE FOR RENT       4,000,000         1            0          0   \n",
       "\n",
       "  Bedrooms Bathrooms    Toilets   City  Neighborhood  \n",
       "0   4 beds   4 baths  5 Toilets  Lekki        Agungi  \n",
       "1   4 beds   4 baths  5 Toilets  Lekki   Other Lekki  \n",
       "2   2 beds   3 baths  3 Toilets  Lekki  Osapa London  \n",
       "3   2 beds   3 baths  3 Toilets  Lekki       Ologolo  \n",
       "4   4 beds   5 baths  5 Toilets  Lekki       Chevron  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locating non-residential building in the dataset\n",
    "commercial_locations= df[df['More Info'].str.contains('COMMERCIAL|WAREHOUSE|CONFERENCE|LAND|CHURCH|EVENT CENTRE|WORKING SPACE|SCHOOL|OFFICE', case=False)]\n",
    "df = df.drop(commercial_locations.index, axis=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ee509e",
   "metadata": {},
   "source": [
    "### 2.3 Removing unwanted columns\n",
    "\n",
    "Details in the Title and More Info column are already summarised in the Neighborhood column, thus we have no use for those two columns, we will be dropping them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb7bc479",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Serviced</th>\n",
       "      <th>Newly Built</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5,000,000/year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>4 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Agungi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5,000,000/year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>4 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Other Lekki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3,500,000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>3 baths</td>\n",
       "      <td>3 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Osapa London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2,700,000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 beds</td>\n",
       "      <td>3 baths</td>\n",
       "      <td>3 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Ologolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4 beds</td>\n",
       "      <td>5 baths</td>\n",
       "      <td>5 Toilets</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Chevron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Serviced  Newly Built  Furnished Bedrooms Bathrooms  \\\n",
       "0  5,000,000/year         0            1          0   4 beds   4 baths   \n",
       "1  5,000,000/year         0            1          0   4 beds   4 baths   \n",
       "2       3,500,000         1            0          0   2 beds   3 baths   \n",
       "3       2,700,000         1            1          0   2 beds   3 baths   \n",
       "4       4,000,000         1            0          0   4 beds   5 baths   \n",
       "\n",
       "     Toilets   City  Neighborhood  \n",
       "0  5 Toilets  Lekki        Agungi  \n",
       "1  5 Toilets  Lekki   Other Lekki  \n",
       "2  3 Toilets  Lekki  Osapa London  \n",
       "3  3 Toilets  Lekki       Ologolo  \n",
       "4  5 Toilets  Lekki       Chevron  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Droping the Title and More Info column as they are not reqiured for this analysis(a repeat)\n",
    "df = df.drop(['Title', 'More Info'], axis=1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bb36ab",
   "metadata": {},
   "source": [
    "### 2.4 Cleaning the Numeric Columns\n",
    "\n",
    "The Price, Bedrooms, Bathrooms and Toilets column contain string variables which are not useful for our ML model, also, they are all object type. We will be extracting just the numberic values from these columns, and converting their type to integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94ccc243",
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   "outputs": [
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       "      <th>Furnished</th>\n",
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       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Agungi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Other Lekki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3500000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Osapa London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Ologolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Chevron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Serviced  Newly Built  Furnished Bedrooms Bathrooms Toilets  \\\n",
       "0  5000000         0            1          0        4         4       5   \n",
       "1  5000000         0            1          0        4         4       5   \n",
       "2  3500000         1            0          0        2         3       3   \n",
       "3  2700000         1            1          0        2         3       3   \n",
       "4  4000000         1            0          0        4         5       5   \n",
       "\n",
       "    City  Neighborhood  \n",
       "0  Lekki        Agungi  \n",
       "1  Lekki   Other Lekki  \n",
       "2  Lekki  Osapa London  \n",
       "3  Lekki       Ologolo  \n",
       "4  Lekki       Chevron  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the Price column to integers\n",
    "\n",
    "# Extracting the numeric values in the columns\n",
    "df['Bedrooms']= df['Bedrooms'].str.extract(r'(\\d+) beds')\n",
    "df['Toilets']= df['Toilets'].str.extract(r'(\\d+) Toilets')\n",
    "df['Bathrooms']= df['Bathrooms'].str.extract(r'(\\d+) baths')\n",
    "df['Price']= df['Price'].str.extract(r'([\\d,]+)')\n",
    "\n",
    "# Removing the comma seperators from the price column\n",
    "df['Price']= df['Price'].str.replace(',', '')\n",
    "                                                                    \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f52742c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price              0\n",
       "Serviced           0\n",
       "Newly Built        0\n",
       "Furnished          0\n",
       "Bedrooms         731\n",
       "Bathrooms       1482\n",
       "Toilets         1652\n",
       "City               0\n",
       "Neighborhood       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NA values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1628ba2d",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "There appears to be null values in the bedrooms,bathroom, and toilets column. We will be dropping all NA rows.\n",
    "\n",
    "###### Dropping rows with na values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96b52244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price           0\n",
       "Serviced        0\n",
       "Newly Built     0\n",
       "Furnished       0\n",
       "Bedrooms        0\n",
       "Bathrooms       0\n",
       "Toilets         0\n",
       "City            0\n",
       "Neighborhood    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping NA values in the columns\n",
    "df.dropna(subset=['Bedrooms', 'Toilets','Bathrooms' ], inplace=True)\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "971a003d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50045 entries, 0 to 53069\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Price         50045 non-null  int64 \n",
      " 1   Serviced      50045 non-null  int64 \n",
      " 2   Newly Built   50045 non-null  int64 \n",
      " 3   Furnished     50045 non-null  int64 \n",
      " 4   Bedrooms      50045 non-null  int64 \n",
      " 5   Bathrooms     50045 non-null  int64 \n",
      " 6   Toilets       50045 non-null  int64 \n",
      " 7   City          50045 non-null  object\n",
      " 8   Neighborhood  50045 non-null  object\n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Changing the datatypes for the columns\n",
    "data_types = {\n",
    "    'Price': int,\n",
    "    'Bedrooms': int,\n",
    "    'Bathrooms': int,\n",
    "    'Toilets': int\n",
    "}\n",
    "\n",
    "df= df.astype(data_types)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c54a15",
   "metadata": {},
   "source": [
    "We have now succefully cleaned up the affected columns.\n",
    "\n",
    "### 2.5 Checking the Overall Statistics of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb1a0378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Serviced</th>\n",
       "      <th>Newly Built</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.004500e+04</td>\n",
       "      <td>50045.000000</td>\n",
       "      <td>50045.000000</td>\n",
       "      <td>50045.000000</td>\n",
       "      <td>50045.000000</td>\n",
       "      <td>50045.000000</td>\n",
       "      <td>50045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.413863e+07</td>\n",
       "      <td>0.213368</td>\n",
       "      <td>0.290898</td>\n",
       "      <td>0.118573</td>\n",
       "      <td>2.430393</td>\n",
       "      <td>2.289220</td>\n",
       "      <td>2.707263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.602166e+09</td>\n",
       "      <td>0.409689</td>\n",
       "      <td>0.454181</td>\n",
       "      <td>0.323289</td>\n",
       "      <td>1.448520</td>\n",
       "      <td>1.611444</td>\n",
       "      <td>1.891613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.500000e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.700000e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price      Serviced   Newly Built     Furnished      Bedrooms  \\\n",
       "count  5.004500e+04  50045.000000  50045.000000  50045.000000  50045.000000   \n",
       "mean   4.413863e+07      0.213368      0.290898      0.118573      2.430393   \n",
       "std    7.602166e+09      0.409689      0.454181      0.323289      1.448520   \n",
       "min    1.000000e+00      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    9.000000e+05      0.000000      0.000000      0.000000      1.000000   \n",
       "50%    2.000000e+06      0.000000      0.000000      0.000000      3.000000   \n",
       "75%    4.500000e+06      0.000000      1.000000      0.000000      3.000000   \n",
       "max    1.700000e+12      1.000000      1.000000      1.000000     10.000000   \n",
       "\n",
       "          Bathrooms       Toilets  \n",
       "count  50045.000000  50045.000000  \n",
       "mean       2.289220      2.707263  \n",
       "std        1.611444      1.891613  \n",
       "min        0.000000      0.000000  \n",
       "25%        1.000000      1.000000  \n",
       "50%        2.000000      3.000000  \n",
       "75%        3.000000      4.000000  \n",
       "max       10.000000     10.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84fb237b",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "- The median rent price is 2 million naira.\n",
    "- There maximum rent price is over 17 billion naira(A bit unrealistic, given the context of lagos)\n",
    "- The minimum rent price on the other hand is one(1)naira, also very unrealistic\n",
    "- Houses have as many as 10bedrooms, and as little as no bedrooms at all, same can be seen for toilets and bathrooms.\n",
    "\n",
    "### 2.6 Handling outliers in the data set\n",
    "We will be taking out outlines in the numeric columns; Price,Bedrooms,Bathrooms and Toilets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8bd1409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for outliers\n",
    "Numerical_columns = ['Price','Bedrooms','Bathrooms', 'Toilets']\n",
    "fig, axarr = plt.subplots(1, 4, figsize=(15, 5))\n",
    "n = 0\n",
    "\n",
    "for j in Numerical_columns:\n",
    "    ax = axarr[n]\n",
    "    sns.boxplot(y=df[j], color='orange', ax=ax)\n",
    "    ax.set_xlabel(j)\n",
    "    ax.set_ylabel(\"\")\n",
    "    n += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351f7857",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "The dataset contains a number of outliers in the price, bedrooms, bathroom and toilet column. To handle this, we will be using the Interquartile range formula(IQR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37e1bb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a function to take out outliers\n",
    "def remove_outlier(data):\n",
    "    '''\n",
    "    This function returns the total outliers in a dataset column, and replaces the upper and lower outliers\n",
    "    using the Interquartile Range (IQR) Method.\n",
    "    data = df['columns']\n",
    "    '''\n",
    "    #calculationg the third quartile of the column\n",
    "    Q3 = data.quantile(0.75)\n",
    "    \n",
    "    #Calculating the First quartile of the column\n",
    "    Q1 = data.quantile(0.25)\n",
    "    \n",
    "    #Calculating the Interquartile range\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    # Identifying the lower outliers\n",
    "    lower_limit = round(Q1 - 1.5 * IQR)\n",
    "    \n",
    "    #Identifying the upper outliers\n",
    "    upper_limit = round(Q3 + 1.5 * IQR)\n",
    "    \n",
    "    #Replacing the lower outliers with the lower IQR value\n",
    "    data.loc[data> upper_limit] = upper_limit\n",
    "    \n",
    "    #Replacing the upper outliers with the lower IQR value\n",
    "    data.loc[data<lower_limit] = lower_limit\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d706728c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing the outliers in each column\n",
    "df['Price'] = remove_outlier(df['Price'])\n",
    "df['Bedrooms'] = remove_outlier(df['Bedrooms'])\n",
    "df['Bathrooms'] = remove_outlier(df['Bathrooms'])\n",
    "df['Toilets'] = remove_outlier(df['Toilets'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ed1ee12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for outliers\n",
    "Numerical_columns = ['Price','Bedrooms','Bathrooms', 'Toilets']\n",
    "fig, axarr = plt.subplots(1, 4, figsize=(15, 5))\n",
    "n = 0\n",
    "\n",
    "for j in Numerical_columns:\n",
    "    ax = axarr[n]\n",
    "    sns.boxplot(y=df[j], color='orange', ax=ax)\n",
    "    ax.set_xlabel(j)\n",
    "    ax.set_ylabel(\"\")\n",
    "    n += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099433d2",
   "metadata": {},
   "source": [
    "We have how succefull removed outliers from the columns. From the box plot displayed we can already get a view on the distribution of our data. For more clarity, we will plot a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff100ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Price'}>,\n",
       "        <AxesSubplot:title={'center':'Serviced'}>,\n",
       "        <AxesSubplot:title={'center':'Newly Built'}>],\n",
       "       [<AxesSubplot:title={'center':'Furnished'}>,\n",
       "        <AxesSubplot:title={'center':'Bedrooms'}>,\n",
       "        <AxesSubplot:title={'center':'Bathrooms'}>],\n",
       "       [<AxesSubplot:title={'center':'Toilets'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7lSYHHEuNkwYCpUkDzTqGpG0k3Us2DcCtEbEU1xMzM6tDoy8IaNqkgdD8WZ3rNdhswN04e3GjFCme4cRSlMk1wbOfD5djqa5o8Zh1q3qTsyckjUlLbTRq0sC+wSYNbPaszvUabDbobpy9uFGKFE8jYomI5yT1kptcs9vqSbfPfu5YqitaPGbdqt5uTU8aaJZ4ck0zM2ukQX82S7oCmArsKakP+BywAE8aaFbiyTXNzKxhBk3OIuL4Kps8aaAZnlzTzMway2trmpmZmRWIkzMzMzOzAnFyZmZmZlYgTs7MzMzMCsTJmZmZmVmBODkzMzMzKxAnZ2ZmZmYF4uTMzMzMrECcnJmZmZkViJMzMzMzswJxcmZmZmZWIE7OzMzMzArEyZmZmZlZgdSdnEl6o6R7c7cXJH1C0lmSHs+VH5E7Zp6kVZIelPSeXPkkScvTtvMkabgvzMzMzKwT1Z2cRcSDEXFIRBwCTAJ+A1ybNn+5tC0ibgSQdAAwEzgQmA5cIGmbtP+FwCnAxHSbXm9cZmZWPJLGS/qBpJWSVkg6I5XvLulWSQ+lv7vljhnSD3pJ20m6KpUvlTSh5S/UrAEa1a05DXg4In41wD4zgCsj4qWIeBRYBRwqaQywc0TcGREBXAYc3aC4zMysGPqBORHxJmAKcFr60T4XuC0iJgK3pcf1/qCfDTwbEfsBXwbObsULM2u0RiVnM4Erco8/Juk+SZfkfgWNBdbk9ulLZWPT/fJyMzPrEhGxLiLuSfc3ACvJ/q+fASxKuy1i84/zen7Q5891NTDNw2SsE40a7gkkvRp4HzAvFV0IfAGI9Pcc4ENApQoSA5RXeq5TyH4t0dPTQ29vb8WYekbDnIP7a34Nw1UtjpKNGzcOuk+rFCkWKFY8RYrFrJul7sa3AEuBnohYB1kCJ2mvtNtYYEnusNIP95ep/oP+lUaAiOiX9DywB/BUc16JWXMMOzkDDgfuiYgnAEp/AST9B3BDetgHjM8dNw5Ym8rHVSjfSkQsBBYCTJ48OaZOnVoxoPMvv45zljfipdVm9QmV4yjp7e2lWqytVqRYoFjxFCkWs24laUfgGuATEfHCAA1b9fygr+nHfrN/6DfjR17RfjwWKZ5ujKURGczx5Lo0JY0p/QoC3g/cn+5fD3xb0rnA3mTjBO6KiE2SNkiaQvYr6iTg/AbEZWZmBSJpW7LE7PKI+E4qfqL0vZG6LNen8np+0JeO6ZM0CtgFeKY8jmb/0B/sB3s9ivbjsUjxdGMswxpzJml74N3Ad3LF/56uorkPeCfwjwARsQJYDDwAfA84LSI2pWNOBS4iG1PwMHDTcOIyM7NiSWO/LgZWRsS5uU3XA7PS/VnAdbnymekKzH3Z/IN+HbBB0pR0zpPKjimd6xjg9jQuzayjDKvlLCJ+Q9afny87cYD95wPzK5TfDRw0nFjM2kXSeLJBya8D/gAsjIivStoduAqYAKwGPhARz6Zj5pFdWbYJOD0ibk7lk4BLgdHAjcAZERGStkvPMQl4GjguIla36CWaNcJhwInAckn3prLPAAuAxZJmA48Bx0L2g15S6Qd9P1v/oL+UrJ7cxOYf9BcD35S0iqzFbGaTX5NZU7RuYJZZ9ypNEXCPpJ2AZZJuBU4mmyJggaS5ZFMEnFk2RcDewPcl7Z++eEpTBCwhS86mk33xvDJFgKSZZFMEHNfSV2k2DBHxYyqPCYNsOqZKxwzpB31E/I6U3Jl1Mi/fZDZMniLAzMwayS1nZg1UhCkCfCXa8DiW6ooWj1m3cnJm1iBFmCIAfCXacDmW6ooWj1m3cremWQMMNEVA2t6oKQIYaIoAMzPrfE7OzIbJUwSYmVkjuVvTbPg8RYCZmTWMk7MGmDD3uwNun3NwPydX2Gf1giObFZK1kKcIMDOzRnK3ppmZmVmBODkzMzMzKxAnZ2ZmZmYF4uTMzMzMrECcnJmZmZkVyLCSM0mrJS2XdK+ku1PZ7pJulfRQ+rtbbv95klZJelDSe3Llk9J5Vkk6z2sGmpmZ2UjViKk03hkR+fX95gK3RcQCSXPT4zMlHUA2N9OBwN7A9yXtn+Z3upBsLcAlwI3AdDbP72RmZmbWMoNNkVXNpdN3aMjzN6NbcwawKN1fBBydK78yIl6KiEeBVcChaVmbnSPizjTj+WW5Y8zMzMxGlOEmZwHcImmZpFNSWU9ahob0d69UPhZYkzu2L5WNTffLy83MzMxGnOF2ax4WEWsl7QXcKumXA+xbaRxZDFC+9QmyBPAUgJ6eHnp7eys+Uc/obFb+oqgWT7X4m2njxo1ted5qihRPkWIxM7ORa1jJWUSsTX/XS7oWOBR4QtKYiFiXuizXp937gPG5w8cBa1P5uArllZ5vIbAQYPLkyTF16tSKcZ1/+XWcs7w4K1PNObi/cjzLX6zrfMNZ9qm3t5dq71s7NDqeescJrF5wZOHeGzMzG5nq7taUtIOknUr3gb8G7geuB2al3WYB16X71wMzJW0naV9gInBX6vrcIGlKukrzpNwxZmZmZiPKcJqXeoBr06wXo4BvR8T3JP0MWCxpNvAYabHmiFghaTHwANAPnJau1AQ4FbgUGE12laav1DQzM7MRqe7kLCIeAd5cofxpYFqVY+YD8yuU3w0cVG8sZmZmZt3CKwSYmZmZFYiTMzMzM7MCcXJmZmZmViBOzszMrOkkXSJpvaT7c2UNW4s5zQRwVSpfKmlCS1+gWQM5OTMzs1a4lGzd5LzSWswTgdvSY8rWYp4OXCBpm3RMaS3mielWOuds4NmI2A/4MnB2016JWZM5OTMzs6aLiDuAZ8qKG7kWc/5cVwPTSq1qZp2mONPom3UwSZcARwHrI+KgVLY7cBUwAVgNfCAink3b5pH90t8EnB4RN6fySWye8+9G4IyICEnbkX0RTQKeBo6LiNUtenlmzbLFWsxpKUDI1ldektuvtObyy1Rfi/mV9Zsjol/S88AewFPlT9rspQCbsQxc0ZaXK1I8zYil3iUgGxWLkzOzxrgU+BpZAlVS6rJZIGluenxmWZfN3sD3Je2fJmUuddksIUvOppNNyvxKl42kmWRdNse15JWZtV49azHXvE5zs5cCXH1C5fMNR9GWlytSPM2I5eQ6lwK8dPoODYnFyZlZA0TEHRUGIM8Apqb7i4Be4ExyXTbAo5JKXTarSV02AJJKXTY3pWPOSue6GviaJKWuHbOa1bv+LGRfPA3WyLWYS8f0SRoF7MLW3ahmHcHJWQcq2H+uVp27bGrU7V0k9SpSdw00JZ7SWswL2Hot5m9LOpesdbm0FvMmSRskTQGWkq3FfH7Zue4EjgFu948X61ROzsxaz102Zbq9i6ReRequgeF12Ui6gqwleU9JfcDnyJKyRq3FfDHwzdQS/QzZ0AGzjuTkzKx53GVjlkTE8VU2NWQt5oj4HSm5M+t0nkrDrHlK3SywdZfNzDRp5r5s7rJZB2yQNCVNAXBS2TGlc7nLxsysi9WdnEkaL+kHklZKWiHpjFR+lqTHJd2bbkfkjhnSjM9mnSJ12dwJvFFSX+qmWQC8W9JDwLvTYyJiBVDqsvkeW3fZXEQ2r9PDbNlls0fqsvkkabJOMzPrPsPp1uwH5kTEPZJ2ApZJujVt+3JEfCm/c53TB5h1BHfZmJlZo9TdchYR6yLinnR/A7CSzVeWVVLPjM9mZmZmI0pDxpyl+Z3eQnZpM8DHJN2XFrotLWT7ylQASWmagLFUnz7AzMzMbEQZ9tWaknYErgE+EREvSLoQ+ALZZf5fAM4BPkR90weUP1dT529qliLFs/6Z5zn/8usG37HMwWN3aUI0jZ83qd73ube3t1DzW5mZ2cg1rORM0rZkidnlEfEdgIh4Irf9P4Ab0sN6pg/YQrPnb2qWOQf3FyaeemNpxtxW0Ph5nOqdw2n1CVMLNb+VmZmNXMO5WlNkV5CtjIhzc+Vjcru9H7g/3a9n+gAzMzOzEWU4zTmHAScCyyXdm8o+Axwv6RCyrsnVwEeg7hmfzczMzEaUupOziPgxlceL3TjAMUOaPsDMzMxspPEKAWZmZmYFUoxR6lZ4E+odaL/gyAZHYmZm1t3ccmZmZmZWIE7OzMzMzArEyZmZmZlZgXjMmTXVYGPV5hzcX3HiWI9VMzOzkcotZ2ZmZmYF4pYzK6R6rw41MzPrdG45MzMzMysQJ2dmZmZmBeLkzMzMzKxAnJyZmZmZFYiTMzMzM7MCKUxyJmm6pAclrZI0t93xmBWR64nZwFxHrBsUIjmTtA3wdeBw4ADgeEkHtDcqs2JxPTEbmOuIdYtCJGfAocCqiHgkIn4PXAnMaHNMZkXjemI2MNcR6wpFSc7GAmtyj/tSmZlt5npiNjDXEesKRVkhQBXKYqudpFOAU9LDjZIerHK+PYGnGhTbsJ1eoHiKFAsUJx6dDVSPZZ+WBlNdIepJeq8arRCfg8SxVPHOsweMpwj1xHWkdYoUT2FiaVQdKUpy1geMzz0eB6wt3ykiFgILBzuZpLsjYnLjwhueIsVTpFigWPEUKZYquraeOJbKihQLFC+eClxHWqRI8XRjLEXp1vwZMFHSvpJeDcwErm9zTGZF43piNjDXEesKhWg5i4h+SR8Dbga2AS6JiBVtDsusUFxPzAbmOmLdohDJGUBE3Ajc2KDTDdpc3WJFiqdIsUCx4ilSLBV1cT1xLJUVKRYoXjxbcR1pmSLF03WxKGKrsZJmZmZm1iZFGXNmZmZmZnRgcjbY0hzKnJe23yfprbUe24RYTkgx3Cfpp5LenNu2WtJySfdKunu4sdQYz1RJz6fnvFfSv9R6bBNi+adcHPdL2iRp97Stoe+NpEskrZd0f5XtLfvMtILryLDiGXF1ZKTVjxLXk7pjGXF1JJ2vtfUkIjrmRjbA82HgDcCrgV8AB5TtcwRwE9l8N1OApbUe24RY3g7slu4fXoolPV4N7Nni92YqcEM9xzY6lrL93wvc3sT35h3AW4H7q2xvyWemFTfXEdeROmIZMfVjiJ+FEVdPXEeKU086reWslqU5ZgCXRWYJsKukMTUe29BYIuKnEfFseriEbM6dZhnO62v5e1PmeOCKYTzfgCLiDuCZAXZp1WemFVxHhhFPk45txPmaVkdGWP0ocT2pM5YmHduI83XV90inJWe1LM1RbZ9GL+sx1PPNJsuqSwK4RdIyZbNVD1et8bxN0i8k3STpwCEe2+hYkLQ9MB24Jlfc6PdmMK36zLSC68jw43Ed2VI31Y8S15PhxeI6srWGfl4KM5VGjWpZmqPaPjUt69HgWLIdpXeSVag/zxUfFhFrJe0F3Crplykzb2Y89wD7RMRGSUcA/w1MrPHYRsdS8l7gJxGR/0XS6PdmMK36zLSC68jw4nEd2Vo31Y8S15P6Y3Edqayhn5dOazmrZWmOavvUtKxHg2NB0p8AFwEzIuLpUnlErE1/1wPXkjV9Dseg8UTECxGxMd2/EdhW0p61vpZGxpIzk7Km6Ca8N4Np1WemFVxHhhGP60hF3VQ/SlxP6ozFdaSqxn5eokUDMBtxI2vpewTYl80D6w4s2+dIthyUd1etxzYhltcDq4C3l5XvAOyUu/9TYHoL3pvXsXluu0OBx9L71PL3Ju23C1kf/g7NfG/SuSZQfSBnSz4zriOuI0WtIyOlfrieuI50Sj1peyWp4805Avgfsqsf/jmVfRT4aLov4Otp+3Jg8kDHNjmWi4BngXvT7e5U/ob0D/QLYEUjYqkxno+l5/sF2aDStw90bDNjSY9PBq4sO67h7w3ZL6p1wMtkv2Jmt+sz4zriOlK0OjLS6ofrietIJ9QTrxBgZmZmViCdNubMzMzMrKs5OTMzMzMrECdnZmZmZgXi5MzMzMysQJycdbk0g/OsGvYLSfs16Dl7JX24EecaZhwDLlRbtu+Xcwvo/o+k51oQorVQIz/jZt1E0smSftzuOGwzJ2dtJGm1pN9K2pi77d3I54iIwyNiUSPP2UEuJVvSY1AR8Y8RcUhEHAKcD3yniXFZjcrqyLOSvitp/OBHmnW3euuGpAnph0qnrRA0ojg5a7/3RsSOuVvNMyq7cg0sKixUK+mPJH0vrbf2I0l/XOHQpi6ga0P23ojYERgDPEGWPDeUpG0afU6zFmh63SinjHOHJvMbXDDp19C7co/PkvStdL/0i2e2pMeA20vN0ZK+lH49PSrp8Nzxr3QxStpP0g8lPS/pKUlXlT39uyQ9lM7zdUnKnedDklambTdL2ie37d2SfpnO+zUqryVWFAuBj0fEJOBTwAX5jel17Qvc3obYbAAR8TvgauAAAEnbpc/9Y5KekPQNSaNL+0v6J0nrJK2V9KH8uSRdKulCSTdKehF4p6Q3pfrynKQVkt6X238XSZdJelLSryR9tvQFlergT1LX+HOSHpH09lS+JnWtz8qd6whJD0jaIOlxSZ9q8ltnXa5C3ThS0s8lvZA+g2fldi+tL/lcanV7W2nDIN8j8yX9BPgN8Ib0Gf9Z+n//Z5Lentt/b0nXS3pG0ipJf5/bdpak/5L0rVQHlkvaX9K8VFfWSPrr3P4npzq1IcV1QoPfvkJyctaZ/hJ4E/Ce9PjPgAeBPYF/By7OJ1Y5XwBuAXYjW9+r/FfWUcCfAm8GPlA6v6Sjgc8AfwO8FvgRqWVJ2Zpq1wCfTc//MHDY8F9i40naEXg78F+S7gX+L9kvzryZwNURsanF4dkgJG0PHEc2KznA2cD+wCHAfsBY4F/SvtPJku93ky3K/C629rfAfGAnYCnw/8jqx17Ax4HLJb0x7Xs+2TIxbyCrfycBf5c7158B9wF7AN8GriSrS/sBHwS+lj5/ABcDH4mInYCD8A8BG6YKdeNFss/ormTLCp2a/h8HeEf6u2vqrbkzPR7se+RE4BSy+rIB+C5wHtln/lzgu5L2SPteQTaL/t7AMcC/SZqWO9d7gW+SfRf9HLiZLB8ZC3ye7P9mJO2QnuPwVF/eTrZCQvcb7pIGvg1rOYjVwEbguXT771T2rtw+ZwHfSvcnkK1m/4bc9pOBVbnH26d9Xpce9wIfTvcvI2s5GlchlgD+PPd4MTA33b8JmJ3b9iqyX0/7kP0HsCS3TWSV8sPtfn9z79n96f7OwLpB9v85ZevX+dbWf798HeknWzD44PQ5exH4o9y+bwMeTfcvARbktu2fPuP7pceXApfltv8F8GvgVbmyK1L92wZ4CTggt+0jQG+6fzLwUG7bwem5enJlTwOHpPuPpeN3bvf761vn3qrVjSr7fgX4crpf+h4Zldtey/fI53PbTyStHZkruzOdZzywibS2Zdr2f4BL0/2zgFtz296bXsc26fFO6bl3JVsX8zngfwGj2/2et/LmlrP2Ozoidk23o2s8Zk3Z41+X7kTEb9LdHdnap8m+1O5K3TYfKtv+69z93+TOsQ/w1dRl8xzZOC6R/crZOx9PZLWrPL5CiIgXgEclHQuvjJ14c2l7aiXZjew/GSuOoyNiV2A7snX9fkj2BbA9sCz3ufweWcsulH0ugV9VOG9++97Amoj4Q9kxY8laEl5ddo7StpIncvd/CxAR5WWl+vS/yNba+5WyYQZvw6w+W9UNSa+T9GeSfpC64Z8nWwNyz0HONdj3SHl9Ka9TpTqxN/BMRGyosK2kvG48FZt7K35beu6IeJGsRfCjwDplFz1UGifcdZycFc+LZF86Ja+rsE9dC6JGxK8j4u8jYm+yX+4XqLapBdaQdcPsmruNjoifki0E+8oVQqkZvBBX00m6gizReqOkPkmzgROA2ZJKC+LOyB1yPNkCul5wtoAiYlNEfIfsV/kUsv/ED8x9JneJbHA0lH0ugddXOmXu/lpgvLYc6Px64HHgKbLFjvepsK2e1/GziJhB1n3632St1GZ1K6sbf07WtX49MD4idgG+weaxwPX+/1ZeX/Yp216qE2uB3SXtVGHb0J804uaIeDfZEJRfAv9Rz3k6jZOz4rkXmClpW0mTyfrrG0LSsZLGpYfPklW2WsZWfQOYJ+nAdJ5dSq1PZOMODpT0N8quHj2dyglly0XE8RExJiK2jYhxEXFxRDwaEdMj4s0RcUBEfD63/1kRMbedMVt1qaVzBlnr5gqy/6S/LGmvtH2spNI4zMXAyZIOSONxPjfI6ZeS/TD6dKp7U8m6W65Mv+gXA/Ml7aTsopFPAt+q4zW8WtIJknaJiJeBF6itDppVVVY3VpJ1DT4TEb+TdCjZ+MqSJ4E/kI2frNeNwP6S/lbSKEnHkV2McENErAF+CvwfSa+R9CfAbODyOl5Xj6T3pbFnL5F1f46I+uLkrHj+P+CPyJKnfyX7BdQofwoslbSR7FfVGRHx6GAHRcS1ZIOvr5T0AnA/cHja9hRwLLCAbFzNROAnDYzZ7P+lz+wLZAP4Z0XECuBMYBWwJH0uvw+8ESAibiIbZ3N72mfAQfcR8XvgfWSf66fIruI9KSJ+mXb5OFny9gjwY7J6eUmdr+dEYHWK+aNkFwyY1aNa3fgH4POSNpBdJPNK62zqspwP/CQNCZgy1CeNiKfJLiCbQ/b//qeBo9L3AWS9EBPIWtGuBT4XEbfW8fpelZ5jLdlwmr9Mr63ryT04ZmZmZsXhljMzMzOzAnFyZmZmZlYgTs7MzMzMCsTJmZmZmVmBODkzMzMzK5BR7Q6gXnvuuWdMmDCh4rYXX3yRHXbYobUBVVGkWKBY8XRKLMuWLXsqIl5bcWPBdUo9GSrH3h7dWE9cR4qnW2MfUh1p9/pR9d4mTZoU1fzgBz+ouq3VihRLRLHi6ZRYgLujAJ/5em6dUk+GyrG3RzfWE9eR4unW2IdSR9ytaWZmZlYgTs7MzMzMCsTJmZmZmVmBODkzMzMzK5COvVpzIMsff56T5353yMetXnBkE6Ixs04yoY7/O8D/f9jI4nrSXG45MzMzMysQJ2dmZmZmBeLkzKwBJF0iab2k+3NlX5T0S0n3SbpW0q6pfIKk30q6N92+kTtmkqTlklZJOk+SUvl2kq5K5UslTWj1azQzs9ZwcmbWGJcC08vKbgUOiog/Af4HmJfb9nBEHJJuH82VXwicAkxMt9I5ZwPPRsR+wJeBsxv/EszMrAicnJk1QETcATxTVnZLRPSnh0uAcQOdQ9IYYOeIuDPNJn0ZcHTaPANYlO5fDUwrtaqZmVl36cqrNc0K6EPAVbnH+0r6OfAC8NmI+BEwFujL7dOXykh/1wBERL+k54E9gKfKn0jSKWStb/T09NDb21sxoI0bN1bdVnTNjH3Owf2D71RBrfH4fTezwdScnEnaBrgbeDwijpK0O9mXzQRgNfCBiHg27TuPrBtmE3B6RNycyieRdf+MBm4EzoiIkLQdWSvBJOBp4LiIWN2A12fWdpL+GegHLk9F64DXR8TTqU78t6QDgUotYVE6zQDbtiyMWAgsBJg8eXJMnTq1Yly9vb1U21Z0zYy9nml4AFafMLWm/fy+m9lghtKteQawMvd4LnBbREwEbkuPkXQAMBM4kGy8zAUpsQOPp7ERRtIs4CjghNRVSUS8FBFPp/vLgIeB/clayvJdn+OAtel+HzA+nXMUsAtl3ahmZtYdakrOJI0DjgQuyhXnx8AsYsuxMVemL6BHgVXAoR5PYyONpOnAmcD7IuI3ufLXln6wSHoD2Q+VRyJiHbBB0pT0+T8JuC4ddj0wK90/Bri9lOyZdYIqVzTvLulWSQ+lv7vlts1LVyc/KOk9uXJf0Wxdr9Zuza8AnwZ2ypX1pC8TImKdpL1S+Viywc8lpXEzLzPM8TS1jqXpGV3fuJFmjKUo2hiNIsXTTbFIugKYCuwpqQ/4HNnVmdsBt6bvjyXpysx3AJ+X1E/W9f/RiCi1gp3K5q7/m9IN4GLgm5JWkbWYzaw7WLP2uBT4GtkP85JSD8wCSXPT4zPLemD2Br4vaf+I2MTmHpglZMNjppPVk1d6YCTNJOuBOa4lr8yswQZNziQdBayPiGWSptZwzmpjY4Y9nqbWsTTnX34d5ywf+rUOtY4ZGYqijdEoUjzdFEtEHF+h+OIq+14DXFNl293AQRXKfwccW3eAZm0WEXdUaM2aQfajBrLek16y1uZXemCAR9OPkkMlrSb1wABIKvXA3JSOOSud62rga5LkFmbrRLVkMIcB75N0BPAaYGdJ3wKekDQmtZqNAdan/V8ZG5OUxs3UMp6mz+NpzMxGjJb3wJh1gkGTs4iYR5o8M7WcfSoiPijpi2RjYBakv/mxMd+WdC5Zc/RE4K6I2CRpg6QpwFKy8TTn546ZBdyJx9OYmY10TeuBAU830wjNnHLG7/vw5jlbACyWNBt4jNTlEhErJC0GHiCbPuC0NE4APJ7GzMw2a0sPjKebGb5mTjnj932IyVlE9JKNCSBNBTCtyn7zgfkVyj2exszMSkq9Ju6BMcvxCgFmZtZ0Va5odg+MWQVOzszMrOmqXNEM7oEx24oXPjczMzMrECdnZmZmZgXi5MzMzMysQJycmZmZmRWIkzMzMzOzAnFyZmZmZlYgnkrDrAEkXQIcBayPiINS2e7AVcAEYDXwgYh4Nm2bB8wGNgGnR8TNqXwSm+dwuhE4IyJC0nbAZcAk4GnguIhY3aKXZzYiLX/8+bpmwl+94MgmRFPdhAoxzjm4f9DYWx2n1c4tZ2aNcSkwvaxsLnBbREwEbkuPkXQA2QSZB6ZjLpC0TTrmQrI1/yamW+mcs4FnI2I/4MvA2U17JWZm1lZuOTNrgIi4Q9KEsuIZZDOiAywiW/rszFR+ZUS8BDyaZjQ/VNJqYOeIuBNA0mXA0WQzoM8Azkrnuhr4miS1Y3maSr/Sa+Ff6TZSuI7YcLnlzKx5eiJiHUD6u1cqHwusye3Xl8rGpvvl5VscExH9wPPAHk2L3MzM2sYtZ2atpwplMUD5QMdsfXLpFLKuUXp6eujt7a0YxMaNG6tuG8icg/uHfAxQ13NVU2/stWj262tm7M3WybGbdRInZ2bN84SkMRGxTtIYYH0q7wPG5/YbB6xN5eMqlOeP6ZM0CtiFbHHnrUTEQmAhwOTJk2Pq1KkVg+vt7aXatoHUM0AaYPUJQ38uqDbYeRPn/PjFgZ+vzi6iZr++et/3ImhW7JL+Efgw2Q+O5cDfAdvToAtqGh6w1a2WLt9KFzOMtC5fd2uaNc/1wKx0fxZwXa58pqTtJO1LNvD/rtT1uUHSFEkCTio7pnSuY4Db/aVj3UDSWOB0YHK60nkbsgtmGnlBjVlHcXJm1gCSrgDuBN4oqU/SbGAB8G5JDwHvTo+JiBXAYuAB4HvAaRGxKZ3qVOAiYBXwMNnFAAAXA3ukiwc+SfqiMusSo4DRqVV4e7IW4xlkF9KQ/h6d7r9yQU1EPEpWVw5NrdM7R8Sd6YfLZbljzDqKuzXNGiAijq+yaVqV/ecD8yuU3w0cVKH8d8Cxw4nRrIgi4nFJXwIeA34L3BIRt0ja4oIaSfkLapbkTlG6cOZlql9QY9ZRBk3OJL0GuAPYLu1/dUR8zhNsmpnZcEnajaw1bF/gOeC/JH1woEMqlA12QU35c9Z00UzP6PovEKlHvRdbVIqxltiHc3FHM9+XSrF3yoUojbpoppaWs5eAv4qIjZK2BX4s6Sbgb8jGAyyQNJesm+XMsvEAewPfl7R/6rYpjQdYQpacTSfrtnllgk1JM8km2Dxu2K/OzMyK7l3AoxHxJICk7wBvp7EX1Gyh1otmzr/8Os5Z3roOpnovmql0Ecucg/sHjb3e56v2nI1SKfbhxNpKjbpoZtAxZ5HZmB5um25BY8cD5M91NTAtDYg2M7Pu9hgwRdL26f/9acBKGntBjVlHqeknQboSZhmwH/D1iFja4PEAW0ywKak0weZTdb0qMzPrCOn75GrgHqAf+DlZq9aOwOJ0cc1jpDGXEbFCUumCmn62vqDmUrKhMzex+YIas45SU3KWPviHSNoVuFbSVgOWc5o2wWazxwk0o0+7aJM2Fikex2JmABHxOeBzZcUv0aALasw6zZA60yPiOUm9ZGPFWj7BZrPHCTSjT7toE04WKR7HYt2k1vUUyyfYHGmTa5rZ4AYdcybptanFDEmjyQZv/hJPsGlmZmbWcLU0L40BFqVxZ68CFkfEDZLupHHjAS4Gvpkm2HyG7GpPMzMzsxFn0OQsIu4D3lKh/Gk8waaZmZlZQ3n5JjMzM7MCcXJmZmZmViBOzszMzMwKxMmZmZmZWYE4OTNrIklvlHRv7vaCpE9IOkvS47nyI3LHzJO0StKDkt6TK58kaXnadp6XODMz605OzsyaKCIejIhDIuIQYBLwG+DatPnLpW0RcSOApAPIppI5kGyy5wvSNDYAF5KtkDEx3aa37pWYmVmrODkza51pwMMR8asB9pkBXBkRL0XEo8Aq4NC0CsfOEXFnmqD5MuDopkdsZmYt5+TMrHVmAlfkHn9M0n2SLpG0WyobC6zJ7dOXysam++XlZmbWZYa+AKWZDZmkVwPvA+aloguBLwCR/p4DfAioNI4sBiiv9FynkHV/0tPTU3VB93oXe59zcP+QjwHqXli+0vP1jB48jkY+XyOVx15vnO1Q72dmMGmJwIvIJikPsrrwIHAVMAFYDXwgIp5N+88DZgObgNMj4uZUPonNq9DcCJzhpQCtEzk5M2uNw4F7IuIJgNJfAEn/AdyQHvYB43PHjQPWpvJxFcq3EhELgYUAkydPjmoLute72PvJNS7wXW71CUN/rmrPN+fgfs5ZPvB/X418vkYqj73eONuh3s9MDb4KfC8ijkk/ZLYHPgPcFhELJM0F5gJnlo3L3Bv4vqT90zKBpXGZS8iSs+lsXibQrGO4W9OsNY4n16WZxpCVvB+4P92/HpgpaTtJ+5IN/L8rItYBGyRNSVdpngRc15rQzZpH0s7AO8jWWCYifh8Rz5GNv1yUdlvE5jGWHpdpXc8tZ2ZNJml74N3AR3LF/y7pELIunNWlbRGxQtJi4AGgHzgttQgAnMrmLpubcIuAdYc3AE8C/ynpzcAy4AygJ/0oISLWSdor7T+WrGWspDT+8mU8LtO6hJMzsyaLiN8Ae5SVnTjA/vOB+RXK7yYbk2PWTUYBbwU+HhFLJX2VrAuzmpaNy6xlbGMjdcq4zGrP2SiVYu+UsZmNGpfp5MzMzNqpD+iLiKXp8dVkydkTksakVrMxwPrc/i0Zl3n+5dcNOraxkTplXGa152yUSrF3ytjMRo3L9JgzMzNrm4j4NbBG0htT0TSybv3rgVmpbBabx1h6XKZ1PbecmZlZu30cuDxdqfkI8HdkjQeLJc0GHgOOBY/LtJHByZmZmbVVRNwLTK6waVqV/T0u07raoN2aksZL+oGklZJWSDojle8u6VZJD6W/u+WOGdLCzal5+qpUvlTShCa8VjMzM7PCq2XMWT8wJyLeBEwBTkuTAM4lmyBwInBbelzvws2zgWcjYj/gy8DZDXhtZmZmZh1n0OQsItZFxD3p/gZgJdncMY2cIDB/rquBaaVWNTMzM7ORZEhXa6buxrcASymbIBDITxA41IWbXzkmIvqB5ymbF8rMzMxsJKj5ggBJOwLXAJ+IiBcGaNiqZ4LAmiYPbPbEgc2Y5K5ZCwXXq0jxOBYzM7Ot1ZScSdqWLDG7PCK+k4obOUFg6Zg+SaOAXYBnyuNo9sSBzZjkrokLBdelSPE4FjMzs63VcrWmyBakXRkR5+Y2NXKCwPy5jgFuT+PSzMzMzEaUWpqXDgNOBJZLujeVfQZYQOMmCLwY+KakVWQtZjOH97LMzMzMOtOgyVlE/JjKY8KgQRMERsTvSMmdmZmZ2UjmtTXNmkzS6jT58r2S7k5lDZvE2czMuouTM7PWeGdEHBIRpSVqGjmJs5mZdREnZ2bt0chJnM06nqRtJP1c0g3psVuXbcTywudmzRfALZIC+L9pSpgtJnGWlJ/EeUnu2NJkzS9TfRLnLdQ6H2C9c7vVM4cg1D+PYKXnq2Uuw0Y+XyOVx95J8+s1eT7AM8hWoNk5PS61Li+QNDc9PrOsdXlv4PuS9k8XnpVal5cAN5K1Lt+EWYdxcmbWfIdFxNqUgN0q6ZcD7FvPJM5bFtY4H2C9c7udPPe7Qz4G6p9HsNLzzTm4f9C5DBv5fI1UHnsz5ldslmbNByhpHHAk2YVkn0zFM4DSky0CeoEzybUuA4+mq/wPlbSa1LqczllqXXZyZh3HyZlZk0XE2vR3vaRrgUNp7CTOdVn++PNNT0TMavQV4NPATrmyprUumxWdkzOzJpK0A/CqiNiQ7v818Hk2T7y8gK0ncf62pHPJumxKkzhvkrRB0hSytW1PAs5v7asxazxJRwHrI2KZpKm1HFKhbEity81eCrBendL1X+05G6VS7J3S/d+orn8nZ2bN1QNcm8YljwK+HRHfk/QzGjeJs1knOwx4n6QjgNcAO0v6Fk1sXW72UoD16pSu/2rP2SiVYu+U7v9Gdf07OTNrooh4BHhzhfKnadAkzmadLCLmAfMAUsvZpyLig5K+iFuXbYRycmZmZkXUyCUCzTqKkzMzMyuEiOgluyrTrcs2onkSWjMzM7MCcXJmZmZmViBOzszMzMwKxGPOzMzMrCtNqHdFkwVHNjiSoXHLmZmZmVmBODkzMzMzK5BBkzNJl0haL+n+XNnukm6V9FD6u1tu2zxJqyQ9KOk9ufJJkpanbecpTZkuaTtJV6XypZImNPg1mpmZmXWMWlrOLgWml5XNBW6LiInAbekxkg4AZgIHpmMukLRNOuZCsrXMJqZb6ZyzgWcjYj/gy8DZ9b4YMzMzs043aHIWEXcAz5QVzwAWpfuLgKNz5VdGxEsR8SiwCjg0rYu2c0TcGREBXFZ2TOlcVwPTSq1qZmZmZiNNvWPOeiJiHUD6u1cqHwusye3Xl8rGpvvl5VscExH9wPPAHnXGZWZmZtbRGj2VRqUWrxigfKBjtj65dApZ1yg9PT309vZWDKJndLaq/VBVO99wbNy4sSnnrVeR4hkJsUgaT9ZS/DrgD8DCiPiqpLOAvweeTLt+JiJuTMfMI+vu3wScHhE3p/JJbF438EbgjNQSbWZmXaTe5OwJSWMiYl3qslyfyvuA8bn9xgFrU/m4CuX5Y/okjQJ2YetuVAAiYiGwEGDy5MkxderUisGdf/l1nLN86C9t9QmVzzccvb29VIuzHYoUzwiJpR+YExH3SNoJWCbp1rTtyxHxpfzOZeM29wa+L2n/tLBzadzmErLkbDpe2NnMrOvUm5xdD8wCFqS/1+XKvy3pXLIvlonAXRGxSdIGSVOApcBJwPll57oTOAa43a0B1i1St39pCMAGSSvZ3KVfySvjNoFHJZXGba4mjdsEkFQat+nkbITq1Mk1yw3Qurw7cBUwAVgNfCAink3HuHXZulotU2lcQZY4vVFSn6TZZEnZuyU9BLw7PSYiVgCLgQeA7wGnpV/8AKcCF5FdJPAwm79ULgb2SF9CnyRd+WnWbdI0MW8h+4EC8DFJ96XpakrT0dQzbtOsk5Val98ETAFOSy3IjZwVwKyjDNpyFhHHV9k0rcr+84H5FcrvBg6qUP474NjB4jDrZJJ2BK4BPhERL0i6EPgC2fjKLwDnAB+ivnGb5c/V1LGZ9ap3TF+lGGuJvZHP10jlsbc6zuGMrWzG2MwBWpdnAFPTbouAXuBM3LpsI4DX1jRrMknbkiVml0fEdwAi4onc9v8AbkgP6xm3uYVmj82sV71jOk+u0H035+D+QWNv5PM1UnnsrY5zOGNrmz1OtKx1eYtZASTlZwVYkjus1Ir8MjW2LvsHzPCer9pzNkql2DvlR0yjfsA4OTNrojRn38XAyog4N1c+pvTFA7wfKK3AUc+4TbOOV6F1uequFcqG1LrsHzDDe75qz9kolWLvlB8xjfoB4+TMrLkOA04Elku6N5V9Bjhe0iFkXx6rgY9ANm5TUmncZj9bj9u8lGyw8024u8a6RKXWZRo7K4BZR3FyZtZEEfFjKv+iv3GAY4Y0btOsk1VrXaaxswKYdRQnZ2Zm1k7VWpcXAIvTDAGPkS4cc+uyjQROzqyr1Dv306XTd2hwJGZWiwFal6FBswKYdZp619Y0MzMzsyZwcmZmZmZWIE7OzMzMzArEyZmZmZlZgTg5MzMzMysQJ2dmZmZmBeLkzMzMzKxAPM+ZmZmZWU6758x0y5mZmZlZgTg5MzMzMysQJ2dmZmZmBVKYMWeSpgNfBbYBLoqIBW0OyaxwXE9suOodSwOdsQat64h1g0K0nEnaBvg6cDhwAHC8pAPaG5VZsbiemA3MdcS6RSGSM+BQYFVEPBIRvweuBGa0OSazonE9MRuY64h1haIkZ2OBNbnHfanMzDZzPTEbmOuIdQVFRLtjQNKxwHsi4sPp8YnAoRHx8bL9TgFOSQ/fCDxY5ZR7Ak81KdyhKlIsUKx4OiWWfSLita0MppIurydD5djbo9D1xHVkC469PRpSR4pyQUAfMD73eBywtnyniFgILBzsZJLujojJjQuvfkWKBYoVj2MZsq6tJ0Pl2NujA2J3HUkce3s0KvaidGv+DJgoaV9JrwZmAte3OSazonE9MRuY64h1hUK0nEVEv6SPATeTXf58SUSsaHNYZoXiemI2MNcR6xaFSM4AIuJG4MYGnW7Q5uoWKlIsUKx4HMsQdXE9GSrH3h6Fj9115BWOvT0aEnshLggwMzMzs0xRxpyZmZmZGR2YnEmaLulBSaskza2wXZLOS9vvk/TWWo9tQiwnpBjuk/RTSW/ObVstabmkeyXd3YJYpkp6Pj3fvZL+pdZjmxDLP+XiuF/SJkm7p22Nfl8ukbRe0v1Vtrfs81IUnfq6JI2X9ANJKyWtkHRGu2MaKknbSPq5pBvaHctQSNpV0tWSfpne/7e1O6Zmcz1pj06tI9DgehIRHXMjG+D5MPAG4NXAL4ADyvY5ArgJEDAFWFrrsU2I5e3Abun+4aVY0uPVwJ4tfF+mAjfUc2yjYynb/73A7c14X9L53gG8Fbi/yvaWfF6Kcuvk1wWMAd6a7u8E/E+nxJ57DZ8Evl2pLhb5BiwCPpzuvxrYtd0xNfn1up60L/6OrCMp9obVk05rOatlaY4ZwGWRWQLsKmlMjcc2NJaI+GlEPJseLiGbc6cZhvPaWv6+lDkeuGIYzzegiLgDeGaAXVr1eSmKjn1dEbEuIu5J9zcAK+mg2d8ljQOOBC5qdyxDIWlnsh85FwNExO8j4rm2BtV8ridt0Kl1BBpfTzotOatlaY5q+zR6WY+hnm82WQtNSQC3SFqmbLbq4ag1lrdJ+oWkmyQdOMRjGx0LkrYHpgPX5Iob+b7UolWfl6LoitclaQLwFmBpm0MZiq8Anwb+0OY4huoNwJPAf6buposk7dDuoJrM9aQ9vkJn1hFocD3ptORMFcrKLzettk8txzY6lmxH6Z1kydmZueLDIuKtZN2dp0l6R5NjuYds6Yg3A+cD/z2EYxsdS8l7gZ9ERL5lq5HvSy1a9Xkpio5/XZJ2JEvoPxERL7Q7nlpIOgpYHxHL2h1LHUaRDQ24MCLeArwIdMwYrDq5nrRYh9cRaHA96bTkrJalOartU9OyHg2OBUl/QtZEOyMini6VR8Ta9Hc9cC1ZM3rTYomIFyJiY7p/I7CtpD1rfR2NjCVnJmVdmg1+X2rRqs9LUXT065K0LdkXzuUR8Z12xzMEhwHvk7SarIvsryR9q70h1awP6IuIUuvL1WRfQt3M9aT1OrmOQKPrSbsH0A1xsN0o4BFgXzYP0jywbJ8j2XKA9121HtuEWF4PrALeXla+A7BT7v5PgelNjuV1bJ7X7lDgsfQetfx9SfvtQjYWbIdmvS+5806g+gUBLfm8FOXWya8r/RtdBnyl3bEM83VMpcMGOwM/At6Y7p8FfLHdMTX59bqetPc1dFwdSXE3rJ4UZoWAWkSVpTkkfTRt/wbZzNBHkCVFvwH+bqBjmxzLvwB7ABdIAuiPbEHUHuDaVDYK+HZEfK/JsRwDnCqpH/gtMDOyT1A73heA9wO3RMSLucMb+r4ASLqCrKLvKakP+BywbS6WlnxeiqLDX9dhwInAckn3prLPRNYSbM31ceByZetVPkKqJ93K9cTq1LB64hUCzMzMzAqk08acmZmZmXU1J2dmZmZmBeLkzMzMzKxAnJyZmZmZFYiTsy4l6S8kPZh7vFrSu9oZk5mZmQ3OyVkHkLQxd/uDpN/mHp9Q6ZiI+FFEvLGO55ogKSR11DQrZmZm3cJfwB0gInYs3U+zJ384Ir7fvojMzMysWdxy1sEkbSfpK5LWpttXJG2Xtk1Nk65WOu5VkuZKeljS05IWS9o9bb4j/X0utcy9TdJ+kn4o6XlJT0m6qhWvz8zMbCRyctbZ/plsyaFDgDeTLcv02RqOOx04GvhLYG/gWeDraVtpofFdI2LHiLgT+AJwC7Ab2Rpz5zcmfDMzMyvn5KyznQB8PiLWR8STwL+SLdsxmI8A/xwRfRHxEtkaYMcMMM7sZWAfYO+I+F1E/LgBsZuZmVkFTs46297Ar3KPf5XKBrMP2RqWz0l6DlgJbCJb27KST5MtpnuXpBWSPlR/yGZmZjYQJ2edbS1ZolXy+lQ2mDXA4RGxa+72moh4HNhqsdWI+HVE/H1E7E3W6naBpP0a8QLMzMxsS07OOtsVwGclvVbSnsC/AN+q4bhvAPMl7QOQjp+Rtj0J/AF4Q2lnScdKGpcePkuWwG1q0GswMzOzHE+l0dn+N7AzcF96/F+pbDBfJeumvEXS3sB64Crguoj4jaT5wE8kbQtMB/4U+IqkXYAngDMi4tHGvhQzMzMDUMRWvVhmZmZm1ibu1jQzMzMrECdnZmZmZgXi5MzMzMysQJycmZmZmRWIkzMzMzOzAhl0Kg1JlwBHAesj4qBUtjvZ1AsTgNXAByLi2bRtHjCbbB6s0yPi5lQ+CbgUGA3cSDYdQ6SFui8DJgFPA8dFxOrB4tpzzz1jwoQJFbe9+OKL7LDDDoOdoiWKFAsUK55OiWXZsmVPRcRrWxySmZmNUINOpSHpHcBG4LJccvbvwDMRsUDSXGC3iDhT0gFkE6MeSraM0PeB/SNik6S7gDOAJWTJ2XkRcZOkfwD+JCI+Kmkm8P6IOG6wwCdPnhx33313xW29vb1MnTq1ltffdEWKBYoVT6fEImlZRExubURmZjZSDdqtGRF3AM+UFc8AFqX7i4Cjc+VXRsRLaZLSVcChksYAO0fEnZFlg5eVHVM619XANEmq7+WYmZmZdbZ6VwjoiYh1ABGxTtJeqXwsWctYSV8qezndLy8vHbMmnatf0vPAHsBT5U8q6RTgFICenh56e3srBrdx48aq21qtSLFAseJxLGZmZltr9PJNlVq8YoDygY7ZujBiIbAQsm7Nat1QndJd1g5FisexmJmZba3eqzWfSF2VpL/rU3kfMD633zhgbSofV6F8i2MkjQJ2YetuVDMzM7MRod6Ws+uBWcCC9Pe6XPm3JZ1LdkHAROCudEHABklTgKXAScD5Zee6EzgGuD284GfTLH/8eU6e+90hH7d6wZFNiMbMzMzK1TKVxhXAVGBPSX3A58iSssWSZgOPAccCRMQKSYuBB4B+4LSI2JROdSqbp9K4Kd0ALga+KWkVWYvZzIa8MjMzM7MONGhyFhHHV9k0rcr+84H5FcrvBg6qUP47UnJnZmZmNtJ5hQAzMzOzAnFyZmZmZlYgjZ5KoxA86N3MzMw6lVvOzMzMzAqkK1vOOsWEOlr3wC18ZmZm3cwtZ2ZmZmYF4uTMzMzMrECcnJmZmZkViJMzMzMzswJxcmZmZmZWIE7OzMzMzArEyZmZmZlZgTg5MzMzMysQJ2dmZmZmBTKs5EzSP0paIel+SVdIeo2k3SXdKumh9He33P7zJK2S9KCk9+TKJ0lanradJ0nDicvMzMysU9WdnEkaC5wOTI6Ig4BtgJnAXOC2iJgI3JYeI+mAtP1AYDpwgaRt0ukuBE4BJqbb9HrjMjMzM+tkw+3WHAWMljQK2B5YC8wAFqXti4Cj0/0ZwJUR8VJEPAqsAg6VNAbYOSLujIgALssdY2ZmZjai1J2cRcTjwJeAx4B1wPMRcQvQExHr0j7rgL3SIWOBNblT9KWysel+ebmZmZnZiDOq3gPTWLIZwL7Ac8B/SfrgQIdUKIsByis95ylk3Z/09PTQ29tb8Yl6RsOcg/sHCKWyaucbjo0bN1Y9bz0xwvDi7JT3ptWKFIuZmY1sdSdnwLuARyPiSQBJ3wHeDjwhaUxErEtdluvT/n3A+Nzx48i6QfvS/fLyrUTEQmAhwOTJk2Pq1KkVAzv/8us4Z/nQX9rqEyqfbzh6e3upFufJc79b1zmHE2envDetVqRYzMxsZBvOmLPHgCmStk9XV04DVgLXA7PSPrOA69L964GZkraTtC/ZwP+7UtfnBklT0nlOyh1jZmZmNqLU3XIWEUslXQ3cA/QDPydr1doRWCxpNlkCd2zaf4WkxcADaf/TImJTOt2pwKXAaOCmdDMzMzMbcYbTrUlEfA74XFnxS2StaJX2nw/Mr1B+N3DQcGIxMzMz6wZeIcDMzMysQJycmZmZmRWIkzMzMzOzAnFyZmZmZlYgTs7MzMzMCmRYV2vayDGh3glzFxzZ4EjMzMy6m1vOzMzMzArEyZmZmZlZgbhbM8ddd2ZmZtZubjkzMzMzKxAnZ2ZmZmYF4uTMzMzMrECcnJmZmZkViJMzMzMzswJxcmZmZmZWIMNKziTtKulqSb+UtFLS2yTtLulWSQ+lv7vl9p8naZWkByW9J1c+SdLytO08SRpOXGZmZmadargtZ18FvhcRfwy8GVgJzAVui4iJwG3pMZIOAGYCBwLTgQskbZPOcyFwCjAx3aYPMy4zMzOzjlR3ciZpZ+AdwMUAEfH7iHgOmAEsSrstAo5O92cAV0bESxHxKLAKOFTSGGDniLgzIgK4LHeMmZmZ2YgynBUC3gA8CfynpDcDy4AzgJ6IWAcQEesk7ZX2HwssyR3fl8peTvfLy7ci6RSyFjZ6enro7e2tGFjPaJhzcH99r6oO1eIA2LhxY9Xt9cY40PMNplPem1YrUixmZjayDSc5GwW8Ffh4RCyV9FVSF2YVlcaRxQDlWxdGLAQWAkyePDmmTp1a8YnOv/w6zlneupWpVp9QOQ7IkpNqcZ5c73JRAzzfYDrlvWm1IsViZmYj23DGnPUBfRGxND2+mixZeyJ1VZL+rs/tPz53/DhgbSofV6HczMzMbMSpOzmLiF8DayS9MRVNAx4ArgdmpbJZwHXp/vXATEnbSdqXbOD/XakLdIOkKekqzZNyx5iZmZmNKMPt3/o4cLmkVwOPAH9HlvAtljQbeAw4FiAiVkhaTJbA9QOnRcSmdJ5TgUuB0cBN6WZmZmY24gwrOYuIe4HJFTZNq7L/fGB+hfK7gYOGE4uZmZlZN/AKAWZmZmYF4uTMzMzMrECcnJmZmZkVSOsmvOpiEwaYr2zOwf11z2dmZmZmI49bzszMzMwKxMmZmZmZWYE4OTMzMzMrECdnZmZmZgXi5MzMzMysQJycmZmZmRWIkzMzMzOzAnFyZmZmZlYgTs7MzMzMCsTJmZmZmVmBDDs5k7SNpJ9LuiE93l3SrZIeSn93y+07T9IqSQ9Kek+ufJKk5WnbeZI03LjMzMzMOlEjWs7OAFbmHs8FbouIicBt6TGSDgBmAgcC04ELJG2TjrkQOAWYmG7TGxCXmZmZWccZVnImaRxwJHBRrngGsCjdXwQcnSu/MiJeiohHgVXAoZLGADtHxJ0REcBluWPMzMzMRpThtpx9Bfg08IdcWU9ErANIf/dK5WOBNbn9+lLZ2HS/vNzMzMxsxBlV74GSjgLWR8QySVNrOaRCWQxQXuk5TyHr/qSnp4fe3t6KT9QzGuYc3F9DSM3XjFiqve5atPq9GSjWjRs3Duu1NFKRYjEzs5Gt7uQMOAx4n6QjgNcAO0v6FvCEpDERsS51Wa5P+/cB43PHjwPWpvJxFcq3EhELgYUAkydPjqlTp1YM7PzLr+Oc5cN5aY0z5+D+hsey+oSpdR/b6vdmoFh7e3up9m/YakWKxczMRra6uzUjYl5EjIuICWQD/W+PiA8C1wOz0m6zgOvS/euBmZK2k7Qv2cD/u1LX5wZJU9JVmifljjEzMzMbUZrRhLIAWCxpNvAYcCxARKyQtBh4AOgHTouITemYU4FLgdHATelmZmZmNuI0JDmLiF6gN91/GphWZb/5wPwK5XcDBzUiFjMzM7NO5hUCzMzMzArEyZmZmZlZgTg5MzMzMysQJ2dmZmZmBeLkzMzMzKxAnJyZmZmZFYiTMzMzM7MCcXJmZmZmViBOzszMzMwKxMmZmZmZWYE4OTMzMzMrECdnZmZmZgXi5MzMzMysQJycmZmZmRWIkzMzMzOzAqk7OZM0XtIPJK2UtELSGal8d0m3Snoo/d0td8w8SaskPSjpPbnySZKWp23nSdLwXpaZmZlZZxo1jGP7gTkRcY+knYBlkm4FTgZui4gFkuYCc4EzJR0AzAQOBPYGvi9p/4jYBFwInAIsAW4EpgM3DSM2K4gJc79bdducg/s5ucr21QuObFZIZmZmhVZ3y1lErIuIe9L9DcBKYCwwA1iUdlsEHJ3uzwCujIiXIuJRYBVwqKQxwM4RcWdEBHBZ7hgzMzOzEWU4LWevkDQBeAuwFOiJiHWQJXCS9kq7jSVrGSvpS2Uvp/vl5ZWe5xSyFjZ6enro7e2tGE/P6KxVpgiaEUu1112LTnlvhvMa67Fx48aWP6eZmVklw07OJO0IXAN8IiJeGGC4WKUNMUD51oURC4GFAJMnT46pU6dWfKLzL7+Oc5Y3JO8ctjkH9zc8ltUnTK372E55b4bzGuvR29tLtc+TmZlZKw3rak1J25IlZpdHxHdS8ROpq5L0d30q7wPG5w4fB6xN5eMqlJuZmZmNOMO5WlPAxcDKiDg3t+l6YFa6Pwu4Llc+U9J2kvYFJgJ3pS7QDZKmpHOelDvGzMzMbEQZTv/WYcCJwHJJ96ayzwALgMWSZgOPAccCRMQKSYuBB8iu9DwtXakJcCpwKTCa7CpNX6lpZmZmI1LdyVlE/JjK48UAplU5Zj4wv0L53cBB9cZiZmZm1i28QoCZmZlZgTg5MzMzMysQJ2dmZmZmBeLkzMzMzKxAnJyZmZmZFYiTMzMzM7MCcXJmZmZmViBOzszMzMwKxMmZmZmZWYE4OTMzMzMrECdnZmZmZgXi5MzMzMysQJycmZmZmRWIkzMzMzOzAilMciZpuqQHJa2SNLfd8ZiZmZm1w6h2BwAgaRvg68C7gT7gZ5Kuj4gH2huZtcuEud+t67jVC45scCRmZmatVYjkDDgUWBURjwBIuhKYATg5syGpN6m7dPoODY7EzMysPkXp1hwLrMk97ktlZmZmZiNKUVrOVKEsttpJOgU4JT3cKOnBKufbE3iqQbENy+lNiEVnD+vwrn5v6vXOsweMZZ9WxmJmZiNbUZKzPmB87vE4YG35ThGxEFg42Mkk3R0RkxsXXv2KFAsUKx7HYmZmtrWidGv+DJgoaV9JrwZmAte3OSYzMzOzlitEy1lE9Ev6GHAzsA1wSUSsaHNYZmZmZi1XiOQMICJuBG5s0OkG7fpsoSLFAsWKx7GYmZmVUcRW4+7NzMzMrE2KMubMzMzMzOjC5Kwoy0BJGi/pB5JWSloh6Yx2xZKLaRtJP5d0Q5vj2FXS1ZJ+md6ft7Uxln9M/z73S7pC0mvaFYuZmRl0WXKWWwbqcOAA4HhJB7QpnH5gTkS8CZgCnNbGWErOAFa2OQaArwLfi4g/Bt5Mm2KSNBY4HZgcEQeRXYwysx2xmJmZlXRVckZuGaiI+D1QWgaq5SJiXUTck+5vIEtA2rbqgaRxwJHARe2KIcWxM/AO4GKAiPh9RDzXxpBGAaMljQK2p8L8emZmZq3UbclZIZeBkjQBeAuwtI1hfAX4NPCHNsYA8AbgSeA/UxfrRZLasrBlRDwOfAl4DFgHPB8Rt7QjFjMzs5JuS85qWgaqlSTtCFwDfCIiXmhTDEcB6yNiWTuev8wo4K3AhRHxFuBFoC1jAyXtRtayui+wN7CDpA+2IxYzM7OSbkvOaloGqlUkbUuWmF0eEd9pVxzAYcD7JK0m6+r9K0nfalMsfUBfRJRaEa8mS9ba4V3AoxHxZES8DHwHeHubYjEzMwO6LzkrzDJQkkQ2rmplRJzbjhhKImJeRIyLiAlk78ntEdGWFqKI+DWwRtIbU9E04IF2xELWnTlF0vbp32saxbhgwszMRrDCrBDQCAVbBuow4ERguaR7U9ln0koII93HgctTAv0I8HftCCIilkq6GriH7Oran+OVAszMrM28QoCZmZlZgXRbt6aZmZlZR3NyZmZmZlYgTs7MzMzMCsTJmZmZmVmBODkzMzMzKxAnZ2ZmZmYF4uTMzMzMrECcnJmZmZkVyP8PO3kpQGJNticAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution of our datasets\n",
    "df.hist(figsize =(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d0c7b19",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "Our columns are mostly negatively skewed. However, more concerning is the price distribution. While we have handled outliers, it seems there are more loctions with very unrealistic prices. To ensure our data functions as expected, we would treat this.\n",
    "\n",
    "\n",
    "##### Cleaning the price column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da2f908e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The minimum cost for rent in the dataset is 1naira\n",
      " The maximum cost for rent in the dataset is 9900000naira\n"
     ]
    }
   ],
   "source": [
    "#Identifying the minimum price in the datasets\n",
    "min_price=df['Price'].min()\n",
    "max_price = df['Price'].max()\n",
    "print(f' The minimum cost for rent in the dataset is {min_price}naira')\n",
    "print(f' The maximum cost for rent in the dataset is {max_price}naira')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c3f8da",
   "metadata": {},
   "source": [
    "It is unrealistic that the minimum cost of rent in lagos is below hundred thousand naira(100,000) per annum. Thus, we would be removing all rows with rent price lower than 100,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "889476cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Serviced</th>\n",
       "      <th>Newly Built</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Agungi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Other Lekki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3500000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Osapa London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Ologolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Chevron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Serviced  Newly Built  Furnished  Bedrooms  Bathrooms  Toilets  \\\n",
       "0  5000000         0            1          0         4          4        5   \n",
       "1  5000000         0            1          0         4          4        5   \n",
       "2  3500000         1            0          0         2          3        3   \n",
       "3  2700000         1            1          0         2          3        3   \n",
       "4  4000000         1            0          0         4          5        5   \n",
       "\n",
       "    City  Neighborhood  \n",
       "0  Lekki        Agungi  \n",
       "1  Lekki   Other Lekki  \n",
       "2  Lekki  Osapa London  \n",
       "3  Lekki       Ologolo  \n",
       "4  Lekki       Chevron  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Taking out rows with unrealistic prices\n",
    "Too_cheap_price = df[(df['Price'] <100000)].index\n",
    "df = df.drop(Too_cheap_price, axis=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c019a3a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49835 entries, 0 to 53069\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Price         49835 non-null  int64 \n",
      " 1   Serviced      49835 non-null  int64 \n",
      " 2   Newly Built   49835 non-null  int64 \n",
      " 3   Furnished     49835 non-null  int64 \n",
      " 4   Bedrooms      49835 non-null  int64 \n",
      " 5   Bathrooms     49835 non-null  int64 \n",
      " 6   Toilets       49835 non-null  int64 \n",
      " 7   City          49835 non-null  object\n",
      " 8   Neighborhood  49835 non-null  object\n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Getting the overall information about our dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84499300",
   "metadata": {},
   "source": [
    "## 3.0 Exploratory Data Analysis(EDA)\n",
    "For the EDA analysis we will be looking at the following details:\n",
    "\n",
    "- The average cost of rent in major locations in Lagos\n",
    "- The cheapest locations in lagos\n",
    "- The most expensive locations in lagos\n",
    "- Average cost of rent in these locations(expensive and cheap)\n",
    "- The avarage cost of rent for one bedroom and 2 bedroom flats in these locations (This is a common ask for house seekers in Lagos).\n",
    "- The cost of furnished and unfurnished apartments in cheap and expensive locations.\n",
    "- The cost of serviced and unserviced appartments in cheap and expensive locations.\n",
    "- The cost of newly built house and old houses in the cheap and expensive locations in lagos.\n",
    "- Estimating the relationship between the variables in the data set\n",
    "\n",
    "**NB**: For futher analysis, we will be converting the serviced,furnished and newly built columns to object data types, this will enable us perform specific analysis, after which they'll be converted back to integres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb216628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49835 entries, 0 to 53069\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Price         49835 non-null  int64 \n",
      " 1   Serviced      49835 non-null  object\n",
      " 2   Newly Built   49835 non-null  object\n",
      " 3   Furnished     49835 non-null  object\n",
      " 4   Bedrooms      49835 non-null  int64 \n",
      " 5   Bathrooms     49835 non-null  int64 \n",
      " 6   Toilets       49835 non-null  int64 \n",
      " 7   City          49835 non-null  object\n",
      " 8   Neighborhood  49835 non-null  object\n",
      "dtypes: int64(4), object(5)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Changing the datatypes for the columns\n",
    "data_types = {\n",
    "    'Serviced': object,\n",
    "    'Newly Built': object,\n",
    "    'Furnished': object,\n",
    "}\n",
    "\n",
    "df= df.astype(data_types)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a67a2d",
   "metadata": {},
   "source": [
    "### 3.1 Checking the average cost of rent in popular cities in Lagos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "413118ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ikoyi</td>\n",
       "      <td>8515993.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Island</td>\n",
       "      <td>6143740.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lekki</td>\n",
       "      <td>3744023.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ikeja</td>\n",
       "      <td>3337592.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ojodu</td>\n",
       "      <td>1516381.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gbagada</td>\n",
       "      <td>1469251.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ajah</td>\n",
       "      <td>1460953.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Surulere</td>\n",
       "      <td>1219872.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yaba</td>\n",
       "      <td>1105711.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City       Price\n",
       "0     Ikoyi  8515993.33\n",
       "1    Island  6143740.96\n",
       "2     Lekki  3744023.02\n",
       "3     Ikeja  3337592.71\n",
       "4     Ojodu  1516381.60\n",
       "5   Gbagada  1469251.67\n",
       "6      Ajah  1460953.08\n",
       "7  Surulere  1219872.47\n",
       "8      Yaba  1105711.95"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the cities by the average prices\n",
    "cities = df.groupby('City')['Price'].mean().round(2).sort_values(ascending= False).reset_index()\n",
    "\n",
    "#Visualizing the cities\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='City', y='Price', data= cities, color='Orange')\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Cities')\n",
    "plt.ylabel('Prices')\n",
    "plt.title('Average cost of rent in various cities in lagos')\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "cities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14bdba59",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "The plot shows clearly that locations like Ikoyi, Island,Lekki and Ikeja are are more expensive locations in lagos in terms of rent prices. The most expensive location is Ikoyi, with an average of about 8.5 million naira for annual rent, while yaba seems to be be the cheapest location, and has an average rent cost of about 1.5 million naira, over 7 million difference from the most expensive location.\n",
    "\n",
    "To get better insight from the analysis, we will be spliting the locations into cheap and expensive locations. From the plot, we can easily create this divide. Thus, **Cheap locations** in the dataset will include Ojodu cities, Gbagada, Ajah, Surulere and Yaba. **Expensive locations** will include Ikoyi, Island, Lekki and Ikeja.\n",
    "\n",
    "### 3.2 Cheapest Locations in Lagos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02313151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cost of rent in cheap locations is: Price    1359512.17\n",
      "dtype: float64\n",
      "Top 10 cheapest location in Lagos are: \n",
      "         Neighborhood   Price\n",
      "46971      Other Ajah  120000\n",
      "38515           Aguda  120000\n",
      "38470  Other Surulere  120000\n",
      "43537      Other Ajah  120000\n",
      "38366           Aguda  120000\n",
      "52889           Onike  108000\n",
      "52024           Akoka  100000\n",
      "34895           Ifako  100000\n",
      "50779           Akoka  100000\n",
      "52023           Akoka  100000\n"
     ]
    }
   ],
   "source": [
    "# Isolating the cheap cities in the dataset\n",
    "cheap_houses = df[df['City'].str.contains('Ojodu|Gbagada|Ajah|Surulere|Yaba', case=False)]\n",
    "\n",
    "#Ranking the locations by rent prices\n",
    "cheap_locations = cheap_houses.sort_values(by='Price', ascending=False)[['Neighborhood', 'Price']]\n",
    "\n",
    "print(f'The average cost of rent in cheap locations is: {cheap_locations.mean().round(2)}')\n",
    "print(f'Top 10 cheapest location in Lagos are: \\n{cheap_locations.tail(10)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902924ee",
   "metadata": {},
   "source": [
    "##### Vizualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c904348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PBfasqjv7U+1j95MkSZp11vUZzRcBn6uq7atqpKq2A5YCNwAv7K/V3ArYa2CfZcDu/fILB9q/BRwOkGRfYNO+fT5wcx8ydwb2aHUwkiRJ08m6HjQP44Gzl6cC2wDXApcBnwQuAJb3698DfCTJecCKgf3eAzw7yXeBfYBr+vazgDlJLgHeS3f6XJIkadZbp0+dV9Ve47R9FLq70avq9v70+neAS/v15wE7jbPfjXQBc9SbBpb3ncSyJUmSZoR1OmiuwRlJNgHmAu/tbwqSJEnSBBk0V2G82U5JkiRN3Lp+jaYkSZIaMWhKkiSpCYOmJEmSmjBoSpIkqQmDpiRJkpowaEqSJKkJg6YkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQmDpiRJkpowaEqSJKkJg6YkSZKaMGhKkiSpiTnDLkDjm7/BXPZfMDLsMiRJktaaM5qSJElqwqApSZKkJgyakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqwqApSZKkJgyakiRJasKgKUmSpCZ8BOU0tfyuezlzybJhlyFJkmao6fAoa2c0JUmS1IRBU5IkSU0YNCVJktSEQVOSJElNGDQlSZLUhEFTkiRJTRg0JUmS1IRBU5IkSU0YNCVJktSEQVOSJElNGDQlSZLUhEFTkiRJTRg0JUmS1IRBU5IkSU0YNCVJktSEQVOSJElNGDQlSZLURLOgmWTbJF9JcmWSq5J8JMncft3CJPsNbPvuJG+ZxM/eI8kFSS5K8v0k757Evie1VkmSpNmqSdBMEuA04MtVtSOwEzAPeF+/yUJgv/H3XqvPW29M02eBo6pqIbALcPKD6CtJJm1cksyZrL4kSZJmklYzmnsDd1fV8QBVtQJ4E3Bkko2BY4BD+xnHQ/t9npTkm0l+nOT1ox0l+YMk3+m3/eRoqExye5JjklwA7Dnm8x8FXDf62VV1eb/PSrORSS5LMtK/vp/kWOC7wHZJbh/Y7kVJThh7kEkel+SsJIuTnJdk5779hCQfSnIu8P5VbSdJkjSbtQqaC4DFgw1VdStwDTACvAs4qaoWVtVJ/SY7A88Dngb8VZKHJ3kicCjwzH52cgVweL/9hsBlVfX0qjp/zOf/P+CKJF9K8kdJ1p9AzU8APldVT6mqqyd4nMcBf1pVuwNvAY4dWLcT8Nyq+rM1bCdJkjQrtTqtG6AeRDvAmVV1D3BPkuuBrYDnALsD/9udjWcD4Pp++xXAqeN1VFXHJPk8sA/wUuAwYK811Hx1Vf3PGra5/0CSecAzgFP62gAeMbDJKVW1YgLbDfZ5FHAUwJZbbzPRUiRJkqalVkFzCfDCwYb+lPl2wFV04XGsewaWV/S1BfhsVR09zvZ396fkx1VVVwEfT/JPwC+TbA78ipVncQdnOu8Y28Uqthv1MOCWfqZ1PHdMcLvBmo+jm/1kxwW7riqQS5IkzQitTp2fAzwyycvhNzfrfBA4oaruBG4DNppgPy9K8qi+n82SbL+mnZLsn/unD3ekC663AMuA3fptdgMeu5pufpHkif2NQQePXdlfCrA0ySF9f0ny5LXdTpIkabZpEjSrqujC2SFJrgR+CNwNvL3f5Fy6m38GbwYar5/LgXcCZye5BPgPYOsJlPAyums0LwL+GTi8n/08Fdisb39tX9eqvA04A/gG/Y1F4zgceGWSi+lmcQ98iNtJkiTNGukyoaabHRfsWh8++fRhlyFJkmao/ReMTNlnJVlcVYvGtvtkIEmSJDUxoaCZ5A1JNu6vL/x0ku8m2ad1cZIkSZq5JjqjeWR/U8s+wJbAEcDfNatKkiRJM95Eg+boHdz7AcdX1cUDbZIkSdIDTDRoLk5yNl3Q/FqSjYBftytLkiRJM91Ev7D9lcBC4MdVdWf/5edHNKtKkiRJM96EgmZV/TrJL+i++7LV04QkSZI0i0woNCZ5P3AocDndU3age0TjtxrVJUmSpBluorOTBwFPqKp71rShJEmSBBO/GejHwMNbFiJJkqTZZaIzmncCFyU5B/jNrGZVvb5JVZIkSZrxJho0T+9fkiRJ0oRM9K7zzyaZC+zUN11RVfe1K0uSJEkz3UTvOt8L+CywjO6JQNsleUVVede5JEmSxjXRU+cfBPapqisAkuwEfBHYvVVhkiRJmtkmetf5w0dDJkBV/RDvQpckSdJqTHRG88Iknwb+uX9/OLC4TUkCmL/BXPZfMDLsMiRJktbaRIPma4E/Bl5Pd43mt4BjWxUlSZKkmW+id53fA3yof0mSJElrtNqgmeTkqnpxkkvpnm2+kqratVllkiRJmtHWNKP5hv7P57cuRJIkSbPLau86r6rr+sXXVdXVgy/gde3LkyRJ0kw10a83+t1x2vadzEIkSZI0u6zpGs3X0s1c7pDkkoFVGwHfblmYJEmSZrY1XaP5BeDfgb8F3jbQfltV3dSsKkmSJM14qw2aVbUcWA4cBpDkUcD6wLwk86rqmvYlSpIkaSaa0DWaSQ5IciWwFPhPYBndTKckSZI0rok+GeivgT2Ar1fVU5L8Dv0sp9pYfte9nLlk2bDLkCRpneBjn9uY6F3n91XVjcDDkjysqs4FFrYrS5IkSTPdRGc0b0kyj+4Z559Pcj3wq3ZlSZIkaaab6IzmgcCdwJuAs4CrgANaFSVJkqSZb6Izmo8Crququ4HPJtkA2Aq4sVllkiRJmtEmOqN5CvDrgfcr+jZJkiRpXBMNmnOq6t7RN/3y3DYlSZIkaTaYaND8ZZIXjL5JciBwQ5uSJEmSNBtM9BrN19Ddbf6x/v1PgJe1KUmSJEmzwYSCZlVdBezRf8VRquq2tmVJkiRpppvoIyjnJ/kQ8E3g3CQfTDK/aWWSJEma0SZ6jeZngNuAF/evW4HjWxUlSZKkmW+i12g+rqpeOPD+PUkualCPJEmSZomJzmjeleRZo2+SPBO4q01JkiRJmg0ezF3nnxu4LvNm4BVtSpIkSdJsMNG7zi8Gnpxk4/79rUneCFzSsDZJkiTNYBM9dQ50AbOqbu3fvrlBPZIkSZolHlTQHCOTVoUkSZJmnYcSNGvSqphGkmyb5CtJrkxyVZKPJFntc92T/FuSTZKMJLlsqmqVJEmazlYbNJPcluTWcV63AdtMUY1TJkmA04AvV9WOwE7APOB9q9uvqvarqlvaVyhJkjRzrPZmoKraaKoKmSb2Bu6uquMBqmpFkjcBS5MsBZ4LPBJ4HPClqnorQJJlwKLBjpLsAJwKHAXcBHwM2BK4E3h1Vf1gSo5IkiRpSCb69UbrigXA4sGG/g77a+jGaiHwFOAe4Iok/1BVPxnbSZInACcCR1TVRUnOAV5TVVcmeTpwLF2olSRJmrUMmisL4197Otp+TlUtB0hyObA9MDZobgl8BXhhVS1JMg94BnBKd2YegEeM++HJUXQzoGy59ay7MkGSJK1jDJorWwIMPmqT/rtDtwNW0M1kjlrB+OO3nC58PrPv72HALVW1cE0fXlXHAccB7Lhg11l5s5UkSVp3PJS7zmejc4BHJnk5QJL1gA8CJ9BdWzkR9wIHAS9P8tL+e0eXJjmk7zNJnjzZhUuSJE03Bs0BVVXAwcAhSa4EfgjcDbz9QfZzB/B84E1JDgQOB16Z5GK6Wc4DJ7VwSZKkachT52P0N/ccMM6qE/rX6HbPH1ge6RdvAHbp224Bnjqw/+9NaqGSJEnTnDOakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqwqApSZKkJgyakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqwqApSZKkJgyakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqYs6wC9D45m8wl/0XjAy7DEmSpLXmjKYkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQmDpiRJkpowaEqSJKkJg6YkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQkfQTlNLb/rXs5csmzYZUiS1IyPWp79nNGUJElSEwZNSZIkNWHQlCRJUhMGTUmSJDVh0JQkSVITBk1JkiQ1YdCUJElSEwZNSZIkNWHQlCRJUhMGTUmSJDVh0JQkSVITBk1JkiQ1YdCUJElSEwZNSZIkNWHQlCRJUhMGTUmSJDVh0JQkSVITBs0BSQ5OUkl27t/vleSMB7H/siRbtKtQkiRp5jBoruww4HzgJcMuRJIkaaYzaPaSzAOeCbyScYJmkqcm+V6SHZI8p1++NMlnkjxizLYbJDkryauTzEtyTpLv9tsfOEWHJEmSNFQGzfsdBJxVVT8Ebkqy2+iKJM8APgEcCPwMOAE4tKp+C5gDvHagn3nAV4EvVNU/AXcDB1fVbsDvAB9MkvEKSHJUkguTXLj85hsn+/gkSZKmlEHzfocBJ/bLJ/bvAZ4IHAccUFXXAE8AlvaBFOCzwLMH+vkKcHxVfa5/H+BvklwCfB14NLDVeAVU1XFVtaiqFs3fdPNJOixJkqThmDPsAqaDJJsDewO7JClgPaCAfwOuA9YHnkI3mznubOSAbwP7JvlCVRVwOLAlsHtV3ZdkWd+fJEnSrOaMZudFwOeqavuqGqmq7YClwLOAW4D96WYl9wJ+AIwkeXy/78uA/xzo613AjcCx/fv5wPV9yPwdYPvGxyJJkjQtGDQ7hwFfGtN2KvBSgKr6BXAA8DHgycARwClJLgV+TXf95qA3Ausn+Xvg88CiJBfSzW7+oNExSJIkTSueOgeqaq9x2j4KfHTg/TXAgoFNnjLOPiMDb48YWN7zIRcpSZI0wzijKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqwqApSZKkJgyakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqwqApSZKkJgyakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmDJqSJElqYs6wC9D45m8wl/0XjAy7DEmSpLXmjKYkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQmDpiRJkpowaEqSJKkJg6YkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQkfQTlNLb/rXs5csmzYZUiS1IyPWp79nNGUJElSEwZNSZIkNWHQlCRJUhMGTUmSJDVh0JQkSVITBk1JkiQ1YdCUJElSEwZNSZIkNWHQlCRJUhMGTUmSJDVh0JQkSVITBk1JkiQ1YdCUJElSEwZNSZIkNWHQlCRJUhMGTUmSJDVh0JQkSVITBs01SHL7wPIHkixJ8oHVbP/uJG+ZmuokSZKmrznDLmCG+SNgy6q6Z9iFSJIkTXfOaE5QktOBDYELkhya5IAkFyT5XpKvJ9lqnH1eneTfk2yQ5M1JLutfb5zyA5AkSZpizmhOUFW9IMntVbUQIMmmwB5VVUleBbwV+LPR7ZP8CbAPcBCwC3AE8HQgdGH1P6vqe1N7FJIkSVPHoLn2tgVOSrI1MBdYOrDuZcC1wEFVdV+SZwFfqqo7AJKcBvw2sFLQTHIUcBTAlltv0/4IJEmSGvLU+dr7B+Afq+q36K7dXH9g3WXACF0YhW4Wc42q6riqWlRVi+Zvuvlk1ipJkjTlDJprbz7w0375FWPWfY8ufJ6eZBvgW8BBSR6ZZEPgYOC8KatUkiRpCAyaa+/dwClJzgNuGLuyqs4H3gKcCVwDnAB8B7gA+JTXZ0qSpNkuVTXsGjSOHRfsWh8++fRhlyFJUjP7LxgZdgmaJEkWV9Wise3OaEqSJKkJg6YkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQmDpiRJkpowaEqSJKkJg6YkSZKaMGhKkiSpCYOmJEmSmjBoSpIkqQmDpiRJkpowaEqSJKkJg6YkSZKaMGhKkiSpCYOmJEmSmpgz7AI0vvkbzGX/BSPDLkOSJGmtOaMpSZKkJgyakiRJasKgKUmSpCYMmpIkSWrCoClJkqQmUlXDrkHjSHIbcMWw65jFtgBuGHYRs5xj3Jbj255j3J5j3NZUju/2VbXl2Ea/3mj6uqKqFg27iNkqyYWOb1uOcVuOb3uOcXuOcVvTYXw9dS5JkqQmDJqSJElqwqA5fR037AJmOce3Pce4Lce3Pce4Pce4raGPrzcDSZIkqQlnNCVJktSEQXOaSfJ7Sa5I8qMkbxt2PdNZku2SnJvk+0mWJHlD375Zkv9IcmX/56YD+xzdj+0VSZ430L57kkv7dR9Nkr79EUlO6tsvSDIy5Qc6ZEnWS/K9JGf07x3fSZRkkyT/muQH/e/yno7x5Erypv7viMuSfDHJ+o7xQ5PkM0muT3LZQNuUjGmSV/SfcWWSV0zRIU+pVYzvB/q/Jy5J8qUkmwysm77jW1W+pskLWA+4CtgBmAtcDDxp2HVN1xewNbBbv7wR8EPgScDfA2/r298GvL9fflI/po8AHtuP9Xr9uu8AewIB/h3Yt29/HfCJfvklwEnDPu4hjPObgS8AZ/TvHd/JHd/PAq/ql+cCmzjGkzq+jwaWAhv0708G/tAxfsjj+mxgN+CygbbmYwpsBvy4/3PTfnnTYY/HFI3vPsCcfvn9M2V8hz6Yvlb6xdoT+NrA+6OBo4dd10x5AV8Bfpfui+637tu2pvtO0geMJ/C1fsy3Bn4w0H4Y8MnBbfrlOXRffJthH+sUjum2wDnA3twfNB3fyRvfjelCUMa0O8aTN8aPBn7S/8M5Bzij/wfbMX7oYzvCykGo+ZgObtOv+yRw2LDHYirGd8y6g4HPz4Tx9dT59DL6F+Koa/s2rUE/7f8U4AJgq6q6DqD/81H9Zqsa30f3y2PbV9qnqn4FLAc2b3IQ09OHgbcCvx5oc3wnzw7AL4Hj+8sTPpVkQxzjSVNVPwX+L3ANcB2wvKrOxjFuYSrG1H8nO0fSzVDCNB9fg+b0knHa/FqANUgyDzgVeGNV3bq6Tcdpq9W0r26fWS/J84Hrq2rxRHcZp83xXb05dKfHPl5VTwHuoDvluCqO8YPUXyd4IN0pxW2ADZP8wep2GafNMX5oJnNM1/mxTvIO4FfA50ebxtls2oyvQXN6uRbYbuD9tsDPhlTLjJDk4XQh8/NVdVrf/IskW/frtwau79tXNb7X9stj21faJ8kcYD5w0+QfybT0TOAFSZYBJwJ7J/kXHN/JdC1wbVVd0L//V7rg6RhPnucCS6vql1V1H3Aa8Awc4xamYkzX6X8n+5tzng8cXv25bab5+Bo0p5f/BXZM8tgkc+ku0D19yDVNW/3dc58Gvl9VHxpYdToweqfcK+iu3Rxtf0l/t91jgR2B7/SneG5Lskff58vH7DPa14uAbwz8xz2rVdXRVbVtVY3Q/S5+o6r+AMd30lTVz4GfJHlC3/Qc4HIc48l0DbBHkkf2Y/Mc4Ps4xi1MxZh+Ddgnyab9bPU+fdusl+T3gL8AXlBVdw6smt7jO+yLXX094ALf/ejunr4KeMew65nOL+BZdFP6lwAX9a/96K4zOQe4sv9zs4F93tGP7RX0d9/17YuAy/p1/8j9DzNYHzgF+BHd3Xs7DPu4hzTWe3H/zUCO7+SO7ULgwv73+Mt0d3o6xpM7xu8BftCPzz/T3Z3rGD+0Mf0i3TWv99HNgr1yqsaU7vrEH/WvI4Y9FlM4vj+iu37yov71iZkwvj4ZSJIkSU146lySJElNGDQlSZLUhEFTkiRJTRg0JUmS1IRBU5IkSU0YNCVpBkqyIslFSS5LckqSR65iu/+a6tokaZRBU5JmpruqamFV7QLcC7xmcGWS9QCq6hnDKE6SwKApSbPBecDjk+yV5NwkXwAuBUhy++hGSd6a5NIkFyf5u77tcUnOSrI4yXlJdu7bD+lnSy9O8q1hHJSkmW/OsAuQJK29/jnF+wJn9U1PA3apqqVjttsXOAh4elXdmWSzftVxwGuq6sokTweOBfYG3gU8r6p+mmST9kciaTYyaErSzLRBkov65fOATwPPoHvG8dJxtn8ucHz1z0iuqpuSzOv3OaV7FDLQPZ4R4NvACUlOBk5rcwiSZjuDpiTNTHdV1cLBhj4s3rGK7QOMfebww4BbxvYDUFWv6Wc49wcuSrKwqm58qEVLWrd4jaYkrRvOBo4cvTs9yWZVdSuwNMkhfVuSPLlfflxVXVBV7wJuALYbVuGSZi6DpiStA6rqLOB04ML+lPtb+lWHA69McjGwBDiwb/9Af+PQZcC3gIunuGRJs0Cqxp5JkSRJkh46ZzQlSZLUhEFTkiRJTRg0JUmS1IRBU5IkSU0YNCVJktSEQVOSJElNGDQlSZLUhEFTkiRJTfx/PXVFHmrg76AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the locations with the cheapest rent prices\n",
    "top_10_cheapest = cheap_locations.tail(10)\n",
    "\n",
    "# Ploting a barchart to vizualize the data\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Price', y='Neighborhood', data=top_10_cheapest, color='lightblue')\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Prices')\n",
    "plt.ylabel('Locations')\n",
    "plt.title('Cheapest Locations in Lagos')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805340b1",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "- From the data we can see that the average cost of rent in cheap location is about 1.3 million naira\n",
    "- The cheapest locations in lagos include cities like ifako, akoka, some parts of surulere, and some parts of Ajah\n",
    "\n",
    "\n",
    "### 3.3 Expensive Locations in Lagos\n",
    "As mentioned earlier, expensive locations will include Ikoyi,Island, Ikeja, and Lekki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0d95576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cost of rent in Expensive locations is: Price    4600407.8\n",
      "dtype: float64\n",
      "The most expensive locations in Lagos are: \n",
      "         Neighborhood    Price\n",
      "20960      Old Ikoyi  9900000\n",
      "20738    Other Ikoyi  9900000\n",
      "20740  Banana Island  9900000\n",
      "20741  Banana Island  9900000\n",
      "20742  Banana Island  9900000\n",
      "4636   Lekki Phase 1  9900000\n",
      "4635   Lekki Phase 1  9900000\n",
      "20743      Old Ikoyi  9900000\n",
      "20746  Banana Island  9900000\n",
      "4625   Lekki Phase 1  9900000\n"
     ]
    }
   ],
   "source": [
    "# Isolating expensive locations in the dataset\n",
    "Expensive_houses = df[df['City'].str.contains('Ikoyi|Island|Lekki|Ikeja', case=False)]\n",
    "\n",
    "# Ranking the locations by the average price\n",
    "Expensive_locations = Expensive_houses.sort_values(by='Price', ascending=False)[['Neighborhood', 'Price']]\n",
    "\n",
    "# Printing out the values\n",
    "print(f'The average cost of rent in Expensive locations is: {Expensive_locations.mean().round(2)}')\n",
    "print(f'The most expensive locations in Lagos are: \\n {Expensive_locations.head(10)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb116f7",
   "metadata": {},
   "source": [
    "##### Vizualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a2f16886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the locations with the most expensive rent prices\n",
    "exp_10_cheapest = Expensive_locations.head(10)\n",
    "\n",
    "# Plotting a bar chart to vizualize the data \n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Price', y='Neighborhood', data=exp_10_cheapest, color='lightblue')\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Prices')\n",
    "plt.ylabel('Locations')\n",
    "plt.title('Cheapest Houses by Location')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc95f492",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "- The average cost of houses in expensive areas in lagos is about 4.6 million naira\n",
    "- The most expensive locations in Lagos are old ikoyi, Banana Island, and Lekki phase 1. \n",
    "\n",
    "\n",
    "\n",
    "### 3.4 The Avarage Cost of Rent for One Bedroom and 2 Bedroom flats in These Locations\n",
    "\n",
    "A very common ask by people looking for houses are cost of 1 bed room and 2 bed room apartments. So, to provide a good insight, we wil find the average cost of single bedroom or a two bedroom apartment in the cheap and expensive areas. This should easily give you an insight on how much to put aside when considering houses in these areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "823beb1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In cheap locations,the average cost of One bedroom apartments is 590249.41, and 1179381.83 for two bedroom\n",
      "While in expensive locations, the average cost of rent for One bedroom is 1372837.63, and two bedroom flats cost \n",
      "3325932.02\n"
     ]
    }
   ],
   "source": [
    "# Checking for the average cost of rent for 1 and 2 bedroom flats\n",
    "\n",
    "#CHEAP LOCATIONS\n",
    "\n",
    "# Filtering the data to select just 1 and 2 bedroom flats in cheap locations\n",
    "Selected_bedrooms= cheap_houses[(cheap_houses['Bedrooms'] == 1) | (cheap_houses['Bedrooms'] == 2)]\n",
    "\n",
    "# Grouping the cheap locations by the number of bedrooms, to identify the mean price\n",
    "Selected_bedrooms = Selected_bedrooms.groupby('Bedrooms')['Price'].mean().round(2)\n",
    "\n",
    "# EXPENSIVE LOCATIONS\n",
    "\n",
    "#Filtering the data to select just 1 and 2 bedroom flats in expensive locations\n",
    "sel_bedrooms= Expensive_houses[(Expensive_houses['Bedrooms']== 1) | (Expensive_houses['Bedrooms'] == 2)]\n",
    "\n",
    "# Grouping the expensive locations by the number of bedrooms, to identify the mean price\n",
    "sel_bedrooms= sel_bedrooms.groupby('Bedrooms')['Price'].mean().round(2)\n",
    "\n",
    "#Putting our result in a table\n",
    "print(f'In cheap locations,the average cost of One bedroom apartments is {Selected_bedrooms[1]}, and {Selected_bedrooms[2]} for two bedroom')\n",
    "print(f'While in expensive locations, the average cost of rent for One bedroom is {sel_bedrooms[1]}, and two bedroom flats cost \\n{sel_bedrooms[2]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "982e2304",
   "metadata": {},
   "source": [
    "### 3.5 The Average Cost of Rent for Furnished and Unfurnished Apartments\n",
    "\n",
    "Here we will calculate the average cost of rent for furnished and unfurnished apartment in both cheap and expensive locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0aa3f484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Furnished</th>\n",
       "      <th>Unfurnished</th>\n",
       "      <th>Furnished</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cheap_location</th>\n",
       "      <td>1363886.0</td>\n",
       "      <td>1333058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expensive_location</th>\n",
       "      <td>4574472.0</td>\n",
       "      <td>4836535.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Furnished           Unfurnished  Furnished\n",
       "Cheap_location        1363886.0  1333058.0\n",
       "Expensive_location    4574472.0  4836535.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping cheap locations by the furnished columns\n",
    "cheap_furnished_data = cheap_houses.groupby('Furnished')['Price'].mean().round().rename({0: 'Unfurnished', 1: 'Furnished'})\n",
    "\n",
    "# Grouping expensive locations by the furnished columns\n",
    "exp_furnished_data = Expensive_houses.groupby('Furnished')['Price'].mean().round().rename({0: 'Unfurnished', 1: 'Furnished'})\n",
    "\n",
    "# Concatinating the grouped locations\n",
    "data1 = pd.concat([cheap_furnished_data, exp_furnished_data], axis=1)\n",
    "\n",
    "# Defining the column title for the new table\n",
    "data1.columns = ['Cheap_location', 'Expensive_location']\n",
    "\n",
    "# Converting the data into a dataframe\n",
    "data = pd.DataFrame(data1)\n",
    "\n",
    "# Transposing the dataframe\n",
    "data = data.transpose()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4277206",
   "metadata": {},
   "source": [
    "##### vizualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "912a9bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff4125d6400>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique categories (furnished and unfurnished)\n",
    "categories = cheap_furnished_data.index\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Calculate the x positions for the bars\n",
    "x1 = range(len(categories))\n",
    "x2 = [x + bar_width for x in x1]\n",
    "\n",
    "# Generate the bar plots\n",
    "plt.subplot()\n",
    "plt.bar(x1, cheap_furnished_data, width=bar_width, label='Cheap locations', color='blue')\n",
    "plt.bar(x2, exp_furnished_data, width=bar_width, label='Expensive locations', color='orange')\n",
    "\n",
    "# Annotate the bars with their values\n",
    "for x, y in zip(x1, cheap_furnished_data):\n",
    "    plt.text(x, y, str(y), ha='center', va='bottom', color='black')\n",
    "\n",
    "for x, y in zip(x2, exp_furnished_data):\n",
    "    plt.text(x, y, str(y), ha='center', va='bottom', color='black')\n",
    "\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Average Price')\n",
    "plt.title('Average Price of Furnished vs. Unfurnished Houses')\n",
    "plt.xticks([x + bar_width / 2 for x in x1], categories)\n",
    "plt.legend(bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373d98fd",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "* The plot shows that in cheap locations, unfurnished housed cost about similar cost with furnished apartments(costing about 1.3 million naira). Interestingly, average cost for unfurnished apartments is slighlty higher than that of the furnished apartment(about 30,828 difference).\n",
    "\n",
    "* In expensive locations,furnished appartments cost around 4.8million naira, while unfurnished apartments cost about 4.5million naira. \n",
    "\n",
    "* Also, furnished apartments cost about 200 thousand naira more than the general a verage cost of rent in expensive locations, which is about 4.6 million naira.\n",
    "\n",
    "\n",
    "### 3.6 The Average Cost of Rent for Serviced and Unserviced Apartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4e7ce130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Serviced</th>\n",
       "      <th>Unserviced</th>\n",
       "      <th>Serviced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cheap_location</th>\n",
       "      <td>1342099.0</td>\n",
       "      <td>1559516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expensive_location</th>\n",
       "      <td>4249979.0</td>\n",
       "      <td>5346581.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Serviced            Unserviced   Serviced\n",
       "Cheap_location       1342099.0  1559516.0\n",
       "Expensive_location   4249979.0  5346581.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the cheap location by the serviced column\n",
    "cheap_serviced_data = cheap_houses.groupby('Serviced')['Price'].mean().round().rename({0: 'Unserviced', 1: 'Serviced'})\n",
    "\n",
    "#Grouping the expensive location by the serviced column\n",
    "exp_serviced_data = Expensive_houses.groupby('Serviced')['Price'].mean().round().rename({0: 'Unserviced', 1: 'Serviced'})\n",
    "\n",
    "# Combining the data into a single table\n",
    "serviced = pd.concat([cheap_serviced_data, exp_serviced_data], axis=1)\n",
    "\n",
    "# Defining the column titles for the table\n",
    "serviced.columns = ['Cheap_location', 'Expensive_location']\n",
    "\n",
    "# Converting the table to a pandas dataframe\n",
    "serviced = pd.DataFrame(serviced)\n",
    "\n",
    "# Transposing the table\n",
    "serviced = serviced.transpose()\n",
    "serviced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "723d6b51",
   "metadata": {},
   "source": [
    "##### vizualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45604205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff40f5c16a0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique categories (serviced and unserviced)\n",
    "categories = cheap_serviced_data.index\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Calculate the x positions for the bars\n",
    "x1 = range(len(categories))\n",
    "x2 = [x + bar_width for x in x1]\n",
    "\n",
    "\n",
    "# Generate the bar plots\n",
    "plt.bar(x1, cheap_serviced_data, width=bar_width, label='Cheap locations', color='blue')\n",
    "plt.bar(x2, exp_serviced_data, width=bar_width, label='Expensive locations', color='orange')\n",
    "\n",
    "# Annotate the bars with their values\n",
    "for x, y in zip(x1, cheap_serviced_data):\n",
    "    plt.text(x, y, str(y), ha='center', va='bottom', color='black')\n",
    "\n",
    "for x, y in zip(x2, exp_serviced_data):\n",
    "    plt.text(x, y, str(y), ha='center', va='bottom', color='black')\n",
    "\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Average Price')\n",
    "plt.title('Average Price of Serviced vs. Unserviced Apartments')\n",
    "plt.xticks([x + bar_width / 2 for x in x1], categories)\n",
    "plt.legend(bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e95c9ff",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "* Serviced apartments in cheap location cost about 1.5million naira on average, while unserviced appartments cost about 1.3 million naira. Also, the average cost for serviced apartments in lagos is about 200,000 more expensive than the overall average cost of rent in cheap locations, which is also about 1.3 million naira.\n",
    "\n",
    "* Serviced apartments in expensive areas cost about 5.3 million naira on average, while unserviced location cost about 4.2 million. Serviced apartments in these areas is about 700,000 naira more than the average cost of rent in this location in general, which is about 4.6 million naira|\n",
    "\n",
    "### 3.7 The Average Cost of Rent for Newly built and Old Apartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d34c784b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Newly Built</th>\n",
       "      <th>Old house</th>\n",
       "      <th>Newly Built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cheap_location</th>\n",
       "      <td>1300213.0</td>\n",
       "      <td>1497433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expensive_location</th>\n",
       "      <td>4480753.0</td>\n",
       "      <td>4902791.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Newly Built         Old house  Newly Built\n",
       "Cheap_location      1300213.0    1497433.0\n",
       "Expensive_location  4480753.0    4902791.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the cheap locations by the Newly Built column\n",
    "cheap_new_house = cheap_houses.groupby('Newly Built')['Price'].mean().round().rename({0: 'Old house', 1: 'Newly Built'})\n",
    "\n",
    "#Grouping expensive locations by the Newly Buily Column\n",
    "exp_new_house = Expensive_houses.groupby('Newly Built')['Price'].mean().round().rename({0: 'Old house', 1: 'Newly Built'})\n",
    "\n",
    "# Concatinating the tables\n",
    "newly_built = pd.concat([cheap_new_house, exp_new_house], axis=1)\n",
    "\n",
    "# Defining the column title for the table\n",
    "newly_built.columns = ['Cheap_location', 'Expensive_location']\n",
    "\n",
    "# Converting the table to a pandas dataframe\n",
    "newly_built = pd.DataFrame(newly_built)\n",
    "\n",
    "# Transposing the table\n",
    "newly_built= newly_built.transpose()\n",
    "newly_built"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ec2fa7",
   "metadata": {},
   "source": [
    "##### Vizualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "87709830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff4105c5430>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique categories (No and Yes)\n",
    "categories = cheap_new_house.index\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.35\n",
    "\n",
    "# Calculate the x positions for the bars\n",
    "x1 = range(len(categories))\n",
    "x2 = [x + bar_width for x in x1]\n",
    "\n",
    "\n",
    "# Generate the bar plots\n",
    "plt.bar(x1, cheap_new_house, width=bar_width, label='Cheap location', color='blue')\n",
    "plt.bar(x2, exp_new_house, width=bar_width, label='Expensive location', color='orange')\n",
    "\n",
    "# Annotate the bars with their values\n",
    "for x, y in zip(x1, cheap_new_house):\n",
    "    plt.text(x, y, str(y), ha='center', va='bottom', color='black')\n",
    "\n",
    "for x, y in zip(x2, exp_new_house):\n",
    "    plt.text(x, y, str(y), ha='center', va='bottom', color='black')\n",
    "\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Average Price')\n",
    "plt.title('Average Price of New vs. Old Apartments')\n",
    "plt.xticks([x + bar_width / 2 for x in x1], categories)\n",
    "plt.legend(bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a9db813",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "* The average cost for old houses in cheap location cost is about 1.3 million naira, and newly built houses cost about 1.4 million naira, which is 100,000 naira more than the overall average cost of houses in these areas, which is also about 1.3million naira.\n",
    "\n",
    "* In expensive areas, old houses cost about 4.4 million naira on average, and newly built houses cost about 4.9 million naira, which is about 200,000 naira more than the average cost of houses in these areas.\n",
    "\n",
    "\n",
    "We are now concluded with our EDA analysis. To continue with our model, we will be converting the Serviced, Newly Built, and Furnished columns back to integr data type, examining the relationship between all the variables in the dataset, and saving out cleaned data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9cee864a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Serviced</th>\n",
       "      <th>Newly Built</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Agungi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Other Lekki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3500000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Osapa London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Ologolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Chevron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Serviced  Newly Built  Furnished  Bedrooms  Bathrooms  Toilets  \\\n",
       "0  5000000         0            1          0         4          4        5   \n",
       "1  5000000         0            1          0         4          4        5   \n",
       "2  3500000         1            0          0         2          3        3   \n",
       "3  2700000         1            1          0         2          3        3   \n",
       "4  4000000         1            0          0         4          5        5   \n",
       "\n",
       "   Neighborhood  \n",
       "0        Agungi  \n",
       "1   Other Lekki  \n",
       "2  Osapa London  \n",
       "3       Ologolo  \n",
       "4       Chevron  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing the city column\n",
    "df = df.drop(['City'], axis=1)\n",
    "\n",
    "# Changing the data type of the Serviced, Newly Built, and Furnished columns\n",
    "data_types = {\n",
    "    'Serviced': int,\n",
    "    'Newly Built': int,\n",
    "    'Furnished': int,\n",
    "}\n",
    "\n",
    "df= df.astype(data_types)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f30947d",
   "metadata": {},
   "source": [
    "### 3.8 Examining the Relationship Between Variables in the Datasets\n",
    "To achieve this, we will be using the pandas corr() function, and using seaborn heatmap to get a better vizualization of the correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6e1879f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a correlation heatmap\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e19f92f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Isolating just the Price row in the heatmap\n",
    "Corr_matrix = df.corr()\n",
    "\n",
    "sns.heatmap(Corr_matrix[['Price']], annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e687c73",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "The correlation shows that bedrooms have the most positive relationship with the prices, indicating that the more the number of bedrooms, the liklihood of a higher cost in rent prices. However, most of the features show a poor relationship with prices, and furnished appartment shows a negative  correlation, meaning, prices are not impacted by that feature.\n",
    "\n",
    "\n",
    "Lets examine the relationship between our target variable(price) and other variables that has show a strong correlation to target variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2b2d8789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PRNO+Na3JFWvYkZq8ZP7kZg7EpLweljLM9Gy2wu7jZZbkSuzxBwwvbinm0RX7OPxRsJqcV4TPXJnNvTMHc/XP3gr7ukp/WLFreGrNARK8gs9T7xbVBAI8teaALQ3uFCUcy7YdA0AaZEoaE5QvnBv9OE371rQmV6xhV7n3JfMnU1Ht55/bjnL9yEy6JFlzE9SpUDoMgYDhn9uPsvCNvRSeCjp9HoEbR2exYOYQBvaO79mlsio/vibTLB4JyhXFKZxOH1fcw4cnL/Ds2oO8sLmYcxU1VNYEuHlCrqUx1KlQ4h5jDK/tPMb/vr6XfScuACDA7Cv68a1rhzAoo0tsFbSJtEQvF6v9NKytEzBBuaI4hdcjYR0IuwrdKbGlqibA67uO8ezaQ7x34KM6uc8rHC213l1ZnYpWGNY3LexSh8ZUxB5jDG/uPsH/vr6XXUfP1cmvHdGXb183hGGZ3cK+LskbfqnD7TEVt0/J47E391MTCOCRoEMRMEG5ojhFwahMXthyFGOay63QK9UXdqlDYyrsISc9KexSR6SYiiMflfPn9Yd5buMRTpfVt2vP7p7CvPH9mTs+l95drJeKdzRQU0RmicgeEdkvIvdF2GeaiGwRkZ0i8rbV93C6hG6kXvJWe8yHw2nd45mWyugaY3h770nm/PpffPWPG+scimuG9uHlu6fwuy+Ni+hQACz64scsya2Q1HR9ohW5Fe6ZOQR/wFDtN1TWBO/9AWNbPEU826PTZeOd5Jkvh7e7SPJoifTJrR6RhXPHWpJHYtMD11uShyOebTQSdpWw/9FnRrUq9wcMK3Yd50u/X8/Un73Fb9/+kNNlVXgkeP38w5fH8fmrsnlydSETfrKSUT98jcdX7LWkh2NOhYh4gV8Ds4ERwDwRGdFkn+7Ab4ACY8zlwE1W3ydeS+g6PXa801IZ3c89sZZ/e/o9thWVAjBlUC9e+MZk/nDbeEZmp7c69pef2WBJboXKmvBVBiPJrRDPtu408bzu75Q9RvrkVo/I0O+HP/+R5JFwU5luN2FXCfuW7OjEuQoeW7GPjz/8Jrc/u5HV+05hgN5dErnrmkH8677p/OG28WwvKuXxtz7kYnUwfutitZ/H3txvybFwct5pPLDfGFMIICJLgTnArgb73Az83RhzGMAYc8JBfZQOwqZDwTzqCXk9+c/rhzJuYM8Ya6QoHZdIWVFuz5ZS6pn08JuNHOzJl/XiS5MGMHN4X3ze+rmFp9YcwCPUZZh5xHqGmZNORTZwpMF2ETChyT5DgAQRWQV0BR4zxjzbdCARuQO4AyA311okqtLxGJvbnf+4biiTB/WOyfurPSpuQW1RiQZ/wNA9NYGbrsrhlgkDImbC2ZFh5qRTEW7prunMmw+4CpgBpABrRWSdMabRXIsx5kngSYBx48a5fz5TcZS/fX0yIrFbK1d7VNyC2qICwaD1lnj0C6OZfUU/knwtR6PbkWHmpFNRBPRvsJ0DlITZ55QxpgwoE5HVwGjAWmSI0mHYfewcj7ze8umPpUOhKHZQ7Q/wzr6T/OP9ppdE9xGv2VKdgXMV1fzj/WL+b+2hFvf79JU5UY1nR4ZZVIGaIjJERFaKyI7Q9igR+UErL9sADBaRPBFJBOYCy5rs8yJwtYj4RCSV4PLIB1FrDwzP7GpJbhUno43tivqNRDxFxO8/cZ5vLt7MrEff4fVdxyPuZ8dxj9cIcqf1dtIeuyWH//8SSe4m7DrugYBhXeFp7vvbNsb9aAVfeWYjy7ZGdircYo97fhxej0jySNhxHO3KrvpWhNL2keROcinHZXtRKd95fhsTfrySB17cWVefx+o4Tbln5hAKRmUSMFBZYwiYYOqwlQyzaM/E74D7gWoAY8w2gk5CRIwxNcBdwGsEHYXnjDE7ReROEbkztM8HwKvANuA94CljzI6otQc+OHbektwqsxausiS3gl1Rv5GIh4j4A6fK+Pel73PdwtX8c/tRAPJbqHxpR5T3gqWbLcndwrxF71qSW8VJezxXEb4UcyS5m2hLxoExhm1FZ/nRy7uY+NBK5j65jqUbjlB6sRohGGxs9X3bG7vs7qoHX7MkD4dd2VWPrNhnSe4k0drXxSo/z204wo2/XMONv1rDcxuPcLHaT3KCh5uuyiE9wtSRleO7avcJNh0uZWCvVEZmdWNgr1Q2HS5l1e7ocyii/ZuQaox5r8m0c6tXA2PMcmB5E9kTTbZ/Dvw8Sj3aHe394QxHPirn8ZX7+PvmYvyh9cABPVO5d+Zg5ozJ5rLvLW9lhEuntpdBOLmVXgbtTbjugS3Jldix/8QFlm0p5sWtJRw6Xd7ouZHZ3fj0mGxuHJ1F327JrnEeImGX3Wnvj0tj3/Hz/GndIf6+uZjzlfXHalBGF26dkMtnr8qhW3ICf91UFPb1Vo7votWFJHiF1MSga5Ca6KO8qoZFqwuZNiwjqjGidSpOichlhAItReRzwNGoNVWUECVnL/Krt/bz3IYj1IRmTLK7p3DvjMF8dmx2o/Qmp4iHGRwl/ig5e5GXtpbwjy0lfNCgwitAfp805ozOYs6Y7LjvQaO0Hzc98S4bDtY7bwleYfbIftw6cQAfG9jD9viyI2fK6Z6S0EiWkuCl6Ex5hFc0J1qn4psEI4yHiUgxcAC4Nep3UTo9J85V8JtVH7J4/SGq/cEf78xuydw9YxA3XdWfRBsqTipKe3P6QiXLdxzjxfeL2Xio8T/3funJ3Dg6i0+PyWZ4v64aYKxYptah6N8zhVsnDOCmcf3pmZbo2Pv175HKifMVdTMVECyAldMjNeoxonIqQgWsZopIGuAxxtgTsBAHaO+PtnHqQiVPrPqQ/1t3qG7ds0+XJO6aPoi54/u3muLkBEk+qAwzI+j2mMFJeT3CTjlPyusRA20UgPE/Xlm3fAfQMzWBG0ZlMWdMFmNze+BxYVC0VeyyO+39YZ3rRvTli5MG8PHLereLLc2fms8Dy3ZSXlVDSkIwvbTab5g/NT/qMaLN/viJiHQ3xpQZY86LSA8R+dElax5HONn7I6dHSlh5/whyq8Qyy+FMWRUPv7Kbq3/6Fk+tOUBlTYCeaYn84IbhrP7ONfzb5IEtOhROZiKMzQ0/xpUR5G5hyfzJluRWcdIe4zXjBlrW0W8MaYlePjs2mz9+ZTzvfX8m//PpkYwb2DPqHwGns8DaypL5k/E1+Sg+sW53dvT+sMtG3WCPJWcv8sgbe8noGr5p1/rvzeDJL43j6sF9orIlO+xo2rAMstOT+PBkGTtKzvHhyTKy05OijqeA6LM/ZhtjztZuGGPOAJ+M+l3imEH3hw+iiiS3QtGZ8G1lj0SQWyVWdfIfeX0PU372Jk+8Hawhn56SwHdmDeWd71zD7VfnkxJFIRUnMxGczrpxipEPvGJJbhUn7TEeezbU+AOs3nuyRR1/e8tYNv3XtTzy+TF8YkifS4oJcrs9TnloBTVNwo1qTFBuBTtswC4bjZU9BgKGVXtO8LU/bmTKT9/k8ZX7OHG+eWdRgAk/WWlpbDvsaMHSzc1mpdYeOGMpMy7aeSeviCQZYyoBRCQFsN4TNQ5p+mVqTa7A42/uB6Brso+vXZ3PbR8fSNfkhFZepbTGharwaXOR5Ip1jDFsPnyGZVtKeHnb0UYtocMx+4p+7aRZ7AjXTrsludKcUxcq+evGIhavP9TIMeqVltiqjbUntZlxDcN/jLGWGRetU/EnYKWI/IFgBshXgD9Gr6rSmUhN9PLVKXncPiWf9FR1JhR3Y4xh97HzLNtawrItJRSftWemUOncGGNYf+AjFq87xCs7jtVluwFMzO/FLRNyuf7yTIb8wJ6ZRjuwIzMu2kDNn4nIdoI9OgT4H2NM9BU1lE7Fmu9OdzRCWVHs4NDpMpZtKeHFrSXsb1KRcFhmVz59ZTafGtWPKT99K0YaKvHK02sO8Kf1hyg8WR/kn56SwP8bm8PNE3IZlNElhtpFxuuRsA6ElSrMUYfdGmNeAdzjUrUTPgm/1NE0cKmzUFnjZ+l7R1rcRx0KZ+iS6Am71NElUdNxo+XEuQpe2naUZVuK2VpU2ui5Ab1SmTM6i4IxWQzKsKfMv9I5efDlXXWPx/Tvzi0TcrlxdBbJCe5umFIwKpMXthylaX+yglGZUY/RolMhImuMMVNE5DyNO4wKYIwx3aJXNz5JTgh/IU9O6FwX8qqaAH/ddIRfvrmfY6UVsVanTXiAcFEIneuMdh5Ky6t5ZcdRXtxSwroDpxtdMDO6JnHj6GAK6BXZ6VpLIgwJHqgO84XpZJdAS6QmepkzJptbJuQyMju9Xd7TC4RrUN7ebkyLToUxZkrovtO67Z09OK7GH+Dv7xfz+Mp9dQFGPo80Wh+MNyKdObef0c5ui5fKVT9+gxp/vb2mpyTwySsyuXF0FhPyermywZ6bCOdQtCTvDDStmNqU974/ky5J7Vt/I5xD0ZI8HO0SqCkiHmCbMWakBd2UOMcfMCzbWsyjK/bV9S7weoTPXJnNvTMGc/XPdJ1ZiQ9q/IbURC/XjuhLwegsrh7cRyu4KpapqPazfPtRFq87zKbDLfc9aW+Hwi7aJVDTGBMQka0ikmuMORy9eko8EggYlu84yqNv7GP/yWDwmgjMGZ3FvTOHkKd9CxQX4Q8Y1h9oOQ//8XlXMnN4RqPSw4oSLQdOlbF43SGe31TE2YvVsVbHUdozULMfsFNE3gPqwlmNMQVRv1Oc0lmC44wxvLbzOAtX7GVPg7bxN4zqx4KZgztU4Fq8xlR0FltsjWA78VJe3FLCy9tKIhYPqqVgdFY7adYxyUlPCluTIie945YqqvYHeGPXcRavO8S/Pqx3Wn0e4frLM7llYi43/259DDVsTpIXKsOsdUToiB4WxwM1G/Df0atVj4jMAh4jGCvylDHm4Qj7fQxYB3zBGPP8pbyXU+x4cHbYKms7HpwdA23sxxjDW3tO8Mjre9lRUr9OeN2Iviy4dgjD+3W8WNzCh28Ie04LXV4yuqPbYmvsP3G+LgW0aTvxeMaOf4dOsub+mWHtbs39My2Nk9MjJWxFTLvaEtjFL17bw182HOHkhXpHKrt7CjdPyOWmcTlkdE2OoXaRSUrwUelv3lslKSH6GbqFc8cCm1m27Rj+gMHrEQpGZYbk0dFa9kcycCcwCNgO/N4YE1VzdhHxAr8GrgWKgA0isswYsyvMfj8FXFn3oqVyrvHQtyASxhje2XeKR97Yy5YjZ+vk04dlsGDmEK7IaZ+I5VjQUrlrN/9Ad1RbbImiM+W8tPUoy7YW88HRxn0MB2d0Yc6YLG4cncUnfr4qNgragB3r2E5il9053ZbALn71VrAisEfgmqEZ3DpxAFOH9HGNkxeJcxXhf5ojySOxcO7YqIMyw9GaC/NHoBp4B5gNjADujXLs8cD+UIdTRGQpMAfY1WS/u4G/AR+LclzFBj6/aG1dW12Aqwf3ZsG1Qxib2/E7XmoWhbs5daGS5duDKaCbmrQTz+6eQsGYLApGZ3XIWTQl9mR0TeILH+vP3PG5ZHd31yxKPNCaUzHCGHMFgIj8HnjPwtjZQMMqSUXAhIY7iEg28BlgOi04FSJyB3AHQG5urgUVlEjUOhQT83vyrWuHMj6vZ4w1ih/UHu3nXEU1r+04xrKtJbz74elG/9J7pSXyqVH9KAi1E9daEvWoLVojEDCtNtj6133TSbiExnBKkNacirpQV2NMjcUvc7idm87nPQp81xjjb2lsY8yTwJMA48aNc8ecYJxz1YAefPvaIUwe1DvWqsQdao/28vU/bWLl7hNU1dTPFHVJ8jFrZCZzxmQxKb/XJXX/7AyoLUbHmbIq/ra5iD+tO8TBVuJx1KFoG605FaNFpDZ6T4CU0HY0FTWLgP4NtnOAkib7jAOWhhyK3sAnRaTGGPOPKPVXLpHn75yk//gUV/DKjmDBnSSfhxnDMygYnc20oX1cX9JYcTe1HWcXrzvMy9uPNnJaFedoraJmW77VG4DBIpIHFANzgZubjJ9X+1hEngFedptD8a2Zg3lkxb6wcrey+9g5Hn2juc4N6cwOxfDMrnxw7HxYuZvp3yMlbFCb26LnmxJoJeBw2tA+FIzO4rrLM+O2aFBb8AiEO0RuiQs8GCFbyq3BwecrqvnHlhIWrzvE7gbf8y5JPj5zZTa3TMxl1qPvxFBDZ+iW7AsblNkt2dp3asFSB7M/2kJoueQuglkdXuBpY8xOEbkz9PwTTr23nYRzKGrl98wc0s7atMz+Exd4bOU+Xt5W0izPWKlnTxiHoiW5W4gUJe+26HkI/kvcdfQcy7aW8NLWphOUjXnmtvHtpJU7ieRzuST5g6HfD5/9MfT7/2TPj93jWOwoLmXx+sO8uKWY8qr6gg0js7txy4QBFIzOIq0DO612ZH8sWLqZF7Ycrdv2B0xoe3PUjoWjR9gYsxxY3kQW1pkwxnzZSV06MgdPlfH4yn38Y0tx3YUov09ao7a7Sj3x2vsjHjh4qoxlW0tYFqaduBKfhCuo1JI8Vnzql2vqHicneCgYncWtEwcwKqd77JSKM9ql94fiXo58VM6v3tzP85uL6qLlB/RM5Z4Zg/n0ldlc9r3lrYygKG3nWGkFL28LOhLbmrQTz+udRsHoLB5b2fJynKLYweCMLtw8IZfPjs0hPSUh1urEHe3S+0NxH0dLL/KrN/fz3MYjVIe6L2Z3T+GeGYP47NgcjV5WHOdMWRWv7DjGsq3FrD/wUaPltsxuydw4uh8Fo7MZmd0NEVGnQmkzrQVa/uWOiYzP69mp48XaSnv2/lBcwInzFfzmrQ/583uH675gmd2SuWv6ID4/rr92XoySztjLwA7KKmtY8cFxlm0p4e29J6lpcPHpnpLAJ0f1o2B0FuMH9sTjlihDJe458lE5f37vMM9tONLifhPye7WTRu7EJ1ATZkLBZ+Gr2J69P5QYcvpCJU+8/SH/t+4QFdVBZ6J3l0S+ec0g5o3P1dQ7i9jVy6AzcfeS91mx6zgXq+sX0lMTvVw3oi8FY7KYMkjbiV8qbu+JITQvMFQrd4oaf4A3d59g8frDrN53UgPPo2D/Q+GzdPY/FH0w7cK5Y1m99zVOl9cHd/ZK9bkj+0NpO2fKqvjdO4U88+7BumjmnqmJ3Dktny9OHEhKojoTl0Jn7KHRVmozOBK8wrShGRSMzmLm8L5qgzbg9p4YkX7PnfidP1ZawV82HGHphsMcLa2ok2d2S2bu+P48GiEbT7EnS2feoncbORQAp8trmLfoXZbMnxzVGOpUuJRH3tjL02sOcKEyeILTUxK4Y2o+X548sEOnRSmxwbTyV/Djg3oxZ3Q214/M1AA4xXZW7z3J4vWHWPHBibo1fRGYOrgPt0zIZfqwDHxejzoVLWBHls7aA2csycOhv04u5fFQYFvXZB9fnZLHV6bk0S1ZL+aKvew7fp4XtwQzN1pi8e0T20kjpTPypafr20r1SkvkpnH9uXl8Lrm9UmOolXIpqFPhUtISvdz28Ty+dnU+6anqTCj2ceSjcl7aVsKyLSWNKg4qSiyZkNeTWyYOYNblmRqfE8eoU+FS3vnudHqmJcZaDaWDcPJ8sJ34sq3N24nn9EihYHQWv1n1YYy0Uzo6pRerW3x+xbemMijD3WXyOwOT8nqEXeqYlNcj6jHUqYgBlTV+/tJKepQ6FM7hIXz1zI7236hhO/F/7T/VqOxz7y6JfGpUFgVjsriyf3dERJ0KxXa2HjnL4vWHWl1eU4fCHWSmJ1uSh0Odinak2h/g+U1F/PLNfZScrWj9BYojdOQy3RXVflZ+cIJlW4t5a8/JRgWDuib7mHV5JnPGZDPpsl6WCtooSrSUVdbw0tYSFq8/zPbi0tZfoNhCkleo9DcPuE7yRv891zLdcUKNP8AL7xfz+Mp9dWliPo80Kh6kKG3hrT0nWLalhNd3HqOsQTOl5AQPM4f3pWB0Fp8Y2ockn6aAKs6w59h5Fq8/xN83F9dlrQEMzezKrRNy+a8Xd8ZQu45PUoKXSn/z5mFJFuoYaZlul+MPGF7aWsKjK/Zy8HQ5EGxn/NmxOdwzfTBTf/5WjDVUOgq3/WFD3WOfR7h6cG/mjMnm2hF9NQVZcYyKaj+v7jjG4vWH2HCwfi0+0efhhiv6cevEXMbm9kBE1KloB7ye4MyCMcHZBqsVy7VMt0sJBAyv7DjGoyv2si/UqVEECkZnce+MweT36RJjDTs3w/qmsft48w6uw/qmxUAbexCB8QN7UjAmi9kj+2lMjtIuTHpoJWfK64MwB/ZK5ZYJA/jcVTn0UBtsVxK8gkeCN5GgYxHAkGhh+cP1ZbpFZBbwGOAFnjLGPNzk+VuA74Y2LwBfN8ZsdVInJzHG8Mau4zzyxt5GqXqfvCKTBTOHMLivBiO5gVcXTCP/vn82iqHwhORupPDkhVYD3d69bzr90t1R1lmxhh3/DmPFmfJqfB7h2hF9uXXiACZf1ksbesWIIX27sa3oDGVV9Ve2tEQPg/t2i3qMhXPH8sauV7jQYIwuiR53lOkWES/wa+BaoAjYICLLjDG7Gux2APiEMeaMiMwGngQmOKWTUxhjWLXnJI+8sbdRYNK1I/qyYOYQRmRFf1IV55ny0IpmQZmBkNwt/T9q24m/uKWY7cXnWt1fHYr4xY517Fjx7WuH8IWP9SejW/TZAYozZHZLZG1V4ytbWVWAzG7RzxjNWriqkUMBcKEqwKyFq6L+0+XkTMV4YL8xphBARJYCc4A6p8IY826D/dcBOQ7qYzvGGNbsP8Ujb+zl/cNn6+TThvbhW9cOYVRO95jppkQmXIfSluTtxZmyKpbvOMqyLSW8d7BJO/H0ZI6VasaQ0j74A6audHZL3D1jcDtppLTGyt0n8UqwJ0tdTEVIHi3hloVbkofDSaciG2hYjKGIlmchvgq8Eu4JEbkDuAMgNzfXLv3azBcWreO9gx/VbU8Z1JsF1w7hqgHRFwpR4g877bGssoY3dh3nxS3FvLPvVON24qkJ3HBFP+aMyWbcgB7kf295m95L6XjYfW08eb6S5zYeYcl7hyM2OlPcSVmVH59X8Eh9xZ2ACTTKBmsPnHQqwi2shZ3PE5FrCDoVU8I9b4x5kuDSCOPGjXPNnGCtQzE+ryffvnYIE/J7xVgjpT1oqz1W1vhZtecky7aWsPKD43Xt7CHYTvz6yzMpGJ3FlMG9SfB2tJJcip3YcW00xrC28DSL1x/mtR3HGjm2H7+sF//68LQ9yiqOkpbo5WK1n4ahOAETlLcnTjoVRUD/Bts5QLNoMxEZBTwFzDbGxJX1js3tzrevG6rBSXFGTnpS2KWOnPQkR993zb5TLNtazCs7jnG+oj6fPNHn4ZqhfSgYnc2M4RkkW8grV5RL5Wx5Fc9vKuLP6w9TeKp+ert7SgKfuyqHmyfkkt+nCwPvC99SW3EXt0/J49GV+6j2189MeCQojxafQE0Y19Rn4efNSadiAzBYRPKAYmAucHPDHUQkF/g78EVjzF4HdXGEv319sjoTcciPPjOKLz+zIazcSW79/fq6xx6ByZf1Zs6YLK4fmakdaJV25dvPbeXlbSVUNqi4Oja3O7dMGMANo/qpYxuHjMrpTrLPQ3mDmc9kn8dSbJ/X66GmpnltYa+FGVPHnApjTI2I3AW8RjCl9GljzE4RuTP0/BPAA0Av4DehH+caY8w4p3Sywt7j51n4Rst+jjoU8cldf94UUb7jwdmOvvfY3B7MGZPFJ6/oR5+uzs6MKEok/ra5CIC0JC+fuTKbWyYMYHg/zVKLZ3766m6q/IYkn6euTkWV3/DTV3czbVhGVGP4A+GbFUSSh8PROhXGmOXA8iayJxo8vh243UkdrPLhyQs8tmIfL20raVYAROkYNE2Zak1uF+985xr690x19D0UJRouz+rGzRNymTMmmy5acbVDUHiqDI+AJ/RnVwSMmEZLW62R6PNSU+UPBkQKYIKBkIkWyvurNYU4dLqMx1bu4x/vF9d1c8zrncYBCydEUVpCHQrFLbx89xSdaVWakZro5WKVP5hREfodlJA8Wjq9U1F0ppxfvbmfv24qqis2k9szlXtnDGbOmCwGfT9slquiKErcog5FxyOvVyr7T5YhAVNfptvAoN7R/5npnZbImbIqAqEZCiEY/9XbQsn1TutUHC29yK/f2s/SDUeoCbWLzeqezN3TB/O5q3I0la8D0yXRE3apo0uinnNFUeKT+2YP5z+e38qFyhr8AYPXI3RPSuC+2cOjHkNEEBESvfX9Q/wBY8kJ7XROxYnzFfzmrQ/583uHqQpFufbtlsRd1wzi8x/rr62hOwHd05LwBy5ysUGX4BQf9EjTwEml/emVlsDpsupm8t5pmhGkRM+0YRn84nOjWbS6kKIz5eT0SGX+1PyogzQBzlfWkN09mVMXqqjyB0j0esjsltSolX1rdBqn4vSFShatLuTZtQfrig317pLIN6YN4uYJuZpC1Yno3yOVJJ+H1MR68y+vqiGjq/YvUNqfIX27ceDUBc5X1NRdyLsm+8jrrd2MFWtMG5ZhyYloSv8eqZw4X9Gok7bVa2OHn+89W17Fz1/bzdU/e4snVxdSUR2gR2oC3/vkMN75znS+MiVPHYpOxvyp+VT7DeVVNRgTvK/2G+ZPzY+1akonZP7UfPwBgz9gMMbUPVZ77BxEWliIRdTL/Kn5lF6sZt+J8+w+do59J85TerHaki126JmKhW/s5fdrDtRN3aSnJHDH1Hz+bfJATaPqxEwblsGD0KZpQkWxEwMgoQBKidDPQOmQRDrXsbIBCb25MQaMWHZuOvQv62Mr9wHQJcnHV6fk8dWr87RyoQK0fZpQUexi0epC0lMSGrWvL6+qYdHqQrVRpV1ZtLqQbikJZLbBFju0U5Ga6OW2jw/ka1fn0z01+pQYRVGU9uLImXK6pzT+s5OS4KXoTHmMNFI6K3bYYod2Kt75zjX06qIR/UpzVu0+waLVhRw5U05/Xf5QYkj/HqkaqKm4gv49Utl9rJTSizUETLBGRXqKj2GZ6VGP0aGdCnUolHCs2n2CB5btJMErdE9J4MT5Ch5YtpMHQR0Lpd2ZlN+TtYX1DZqr/X7KqvzcPD43hlop7UWCB6rDdAhIuIQ0irb+Wcrslsjawvr00YCBM+U1ZHaLfqa/w2d/KEpTFq0upKrGz7HSCvYcP8+x0gqqavwsWl0Ya9WUTsiz6w5Zkisdi0CEUMhI8kjU/lk6cb6i0Z+lVbtPRD3Gyt0n8UpwhqK2mqZXgvJoUadC6XTsPX6O02VV1PgNXhFq/IbTZVXsO34u1qopnZBTF6osyZWORW17iGjlkVi0upAEr5Ca6EMkeJ/gFUt/lsqq/Pi8QpLPS3KClySfF59XKKvyRz1Gh17+UJRwVIfKsns89d38AgFDlV8T+RRFiU+OnCnHK1B48kJdbE7vLomWgizTEr1crPbjaTBJEjBBebQ4OlMhIrNEZI+I7BeR+8I8LyLyeOj5bSIy1kl9FAUg0ecBAwFjMBgCJtg9J9GnE3eKosQnXZN8FJ+toCbU96MmYCg+W2GpJtPtU/IIGKgJBAiYQOg+KI8Wx66iIuIFfg3MBkYA80RkRJPdZgODQ7c7gN86pY+i1DI4oyu9uybi8wj+gMHnEXp3TWRwRtdYq6Z0Qnp3CR8EF0muKOEwJjTTahrcGsqj4J6ZQ7h3+iBSErzUBILppPdOH8Q9M4dEPYaTyx/jgf3GmEIAEVkKzAF2NdhnDvCsCX7qdSLSXUT6GWOOOqiX0smZPzWfB5btJDPdR0pCcLpPy3QrsSI5wUt6so/Sivqo+/TkoG0qSrRcqPI3bwbWJclSPAQEHQsrTkRTnHQqsoEjDbaLgAlR7JMNNHIqROQOgjMZ5OZqmpXSNtpaplvtUbGT/j1SOeGrILdXWp0s2iZOaotKLXY0A7MDJxeRw+XDNJ2HiWYfjDFPGmPGGWPG9enTxxbl3MCkvB6W5J2JSIZpl8FOG5bBkjsm8s53p7PkjomWcrnVHhU7aUuDu3iyxZz08HWDIsnD0RFtNCnChFQkeSTc0ijRSaeiCOjfYDsHKLmEfVrk4MM3WJJbxcnxl8yf3OzLMCmvB0vmT27z2OCs7k4f98KHb2hmnJ6Q3M3E8zF30h7j+XvqtO7ThmXwYMHlZHRNpvRiNRldk3mw4PI2F2KzS2+7xllz/8xmDkROehJr7p8Z9Rh22agdn8mu47Lnxzc0cyCSvEG5FZyyI6uIlSAOSwOL+IC9wAygGNgA3GyM2dlgnxuAu4BPElwaedwYM76lcceNG2c2btzoiM6K0oRWq8+oPSrtSIv2qLaotCMRbdGxmApjTI2I3AW8BniBp40xO0XkztDzTwDLCToU+4Fy4Dan9FEURVEUxVkcm6lwChE5CUSqX9sbONWO6tiJ6h4bWtL9lDFmVksvVnt0JR1V9xbtMc5s0U36qC7huSRbjDunoiVEZKMxZlys9bgUVPfY4KTuelxig+refuNeKm7SR3UJz6XqoiUEFUVRFEWxBXUqFEVRFEWxhY7mVDwZawXagOoeG5zUXY9LbFDd22/cS8VN+qgu4bkkXTpUTIWiKIqiKLGjo81UKIqiKIoSI9SpUBRFURTFFjqMUyEis0Rkj4jsF5H7Yq1PtIhIfxF5S0Q+EJGdInJvrHWygoh4ReR9EXk51rpYJdQV93kR2R06/pNsGldtMUbEqz06ZYuhsV1hj260LzfZi5M2cAm6LAidox0iskREou5K1iGcChHxAr8GZgMjgHkiMiK2WkVNDfBtY8xwYCLwzTjSHeBe4INYK3GJPAa8aowZBozGhs+hthhz4tUebbdFcJ09utG+3GQvjtiAVUQkG7gHGGeMGUmwIvbcaF/fIZwKYDyw3xhTaIypApYCc2KsU1QYY44aYzaHHp8naEjZsdUqOkQkB7gBeCrWulhFRLoBU4HfAxhjqowxZ20YWm0xRsSrPTpoi+Aie3SbfbnJXhy2gUvBB6SEenilYqHRZ0dxKrKBIw22i4iji2EtIjIQuBJYH2NVouVR4DtAIMZ6XAr5wEngD6Hpz6dEJM2GcdUWY8ejxKc9OmWL4FJ7dIl9PYp77MVJG7CEMaYY+AVwGDgKlBpjXo/29R3FqQjXMS2ucmVFpAvwN+DfjTHnYq1Pa4jIp4ATxphNsdblEvEBY4HfGmOuBMoAO9ab1RZjQJzbo1O2CC60RzfYlwvtxUkbsISI9CA4m5UHZAFpInJrtK/vKE5FEdC/wXYOFqZrYo2IJBD8ki02xvw91vpEyceBAhE5SHBKdbqI/Cm2KlmiCCgyxtT+U3qe4JfajnHVFtufeLZHp2yxdmzX2KOL7Mtt9uKkDVhlJnDAGHPSGFMN/B2YHO2LO4pTsQEYLCJ5IpJIMKhkWYx1igoREYLraB8YYx6JtT7RYoy53xiTY4wZSPB4v2mMidqbjTXGmGPAEREZGhLNAHbZMLTaYgyIZ3t00BbBRfboJvtym704bANWOQxMFJHU0DmbgYWgUZ9jarUjxpgaEbkLeI1gpOrTxpidMVYrWj4OfBHYLiJbQrLvGWOWx06lTsPdwOLQxbYQuK2tA6otKpeI7bYIrrNHta+WccQGrGKMWS8izwObCWbsvI+Fkt1apltRFEVRFFvoKMsfiqIoiqLEGHUqFEVRFEWxBXUqFEVRFEWxBXUqFEVRFEWxBXUqFEVRFEWxBXUqXIKI+EVki4hsFZHNIhJ1sZHQ638oIv/hlH5Kx8eqDYa6Kn6jwfY0N3R7VDoXItIrZLdbROSYiBQ32E5ssu9TtU3MROSgiPRuZezvOal7R0SdCvdw0RgzxhgzGrgfeMiOQUMNYRQlGqzaYHfgG63s04xQ50xFsQVjzOmQ3Y4BngAW1m6Hmqg13Pd2Y4yVolLqVFhEnQp30g04U7shIv8pIhtEZJuI/HcD+fdFZI+IrACGNpCvEpGfiMjbwL0iMiPUpGa7iDwtIkmh/SLJD4Zev1ZENorIWBF5TUQ+FJE7Q/v0E5HVoX8DO0Tk6nY6Nkr7UGeDItJFRFaGZi+2i0htl8uHgctCNvDzkKyLiDwvIrtFZHGoIl+tTT0gImuAm0RkXmisHSLy09o3bUF+QUR+KiKbRGSFiIwP2XmhiBSE9rlcRN4L6bNNRAa3w3FSXEgL17ZVIjIuzP63NrCdRSLiFZGHCXbq3BKy5TQR+WdoJm+HiHyh3T9YPGCM0ZsLboAf2ALsBkqBq0Ly6whWMxOCTuDLBFvkXgVsJ9iWthuwH/iP0GtWAb8JPU4m2KVwSGj7WeDfI8lDjw8CXw89XghsA7oCfQg24QH4NvD90GMv0DXWx1BvjtmgD+gWetw7ZGsCDAR2NHj9tNDrckK2uhaY0sCmvhN6nEWwFHCf0NhvAp+OJA+9xgCzQ49fAF4HEoDRwJaQ/JfALaHHiUBKrI+p3trdhn8I/KCFa9sqYFzo8cGQPQ8HXgISQvLfAF8KPb7QYOz/B/yuwXZ6rD+vG286U+EeaqeehwGzgGdD//KuC93eJ1g2dRgwGLgaeMEYU26Cnf6a1vP/S+h+KMHmMHtD238k6JREktdSO952YL0x5rwx5iRQISLdCfYUuE1EfghcYYw53+YjoMSaSDYowE9EZBuwgmDr7L4RxnjPGFNkjAkQdFAGNniu1iY/BqwywYZFNcBigrYXSQ5QBbwaerwdeNsEmx1tb/Aea4Hvich3gQHGmIuXeByU+MZLy9e2pswg+CdtgwTLh88g2Iq8KduBmaEZs6uNMaU26txhUKfChRhj1hL0oPsQvKA/ZOrXCAcZY35fu2sLw5SF7sO1Pm5JXktl6D7Q4HHtts8Ys5rgF7UY+D8R+VIr4ylxRBMbvCV0f5UJrlsfJzjTFY6GtuKncX+htthktQn9PaSBTYacF1/o8Z+BAuAi8JqITG9hPKXjUtb6Lo0Q4I8NrrFDjTE/bLpTyEmpnSF+SEQeaLuqHQ91KlyIiAwj6G2fJtgI6Csi0iX0XLaIZACrgc+ISIqIdAVujDDcbmCgiAwKbX8ReLsFebQ6DiC4FPI7gp0HY9WmV3GAJjaYTvBcV4vINcCA0G7nCS6LWWU98AkR6R0K2pxH0PYiyaPVOR8oNMY8TnCmbdQl6KbEP8lYu7atBD4Xuq4iIj1D1zeAagm2a0dEsoByY8yfgF+g17ywaGaAe0iR+s59AvybMcYPvC4iw4G1oZi3C8CtxpjNIvIXglPMh4B3wg1qjKkQkduAv0owE2QD8IQxpjKc3IK+04D/FJHqkE46UxH/hLVBEVkMvCQiG6mPucAYc1pE/iUiO4BXgH9G8ybGmKMicj/wVuh9lhtjXgSIJI+SLwC3hmzyGPCghdcqHYcKgh0+o7q2GWN2icgPCF5rPUA18E2C19UngW0isplgbMbPRSQQ2ufrzn6M+ES7lCqKoiiKYgu6/KEoiqIoii2oU6EoiqIoii2oU6EoiqIoii2oU6EoiqIoii2oU6EoiqIoii2oU6EoiqIoii2oU6EoiqIoii38fwZvV9K7+YXEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 540x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identifying positive correlated variables\n",
    "columns = ['Bedrooms', 'Bathrooms', 'Toilets']\n",
    "\n",
    "# Creating a pair plot\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.pairplot(df, \n",
    "             x_vars=columns, \n",
    "             y_vars='Price',\n",
    "             diag_kind='hist',\n",
    "             kind='reg')\n",
    "#Displaying the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3c34f5",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "All the predictor features seem to have a positive correlation with price.\n",
    "\n",
    "NOTE: Ultimately, it is good practice to drop columns that have negative correlation, as they do not influence the target variable. However, for the sake of our proposed webpage, we'll keeo them.\n",
    "\n",
    "W have now concluded this session of the analysis, we will proceed to save the cleaned CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "351dd6d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the clean dataset\n",
    "df.to_csv('Cleaned_lagos_renewed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "19be301b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49835 entries, 0 to 53069\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Price         49835 non-null  int64 \n",
      " 1   Serviced      49835 non-null  int64 \n",
      " 2   Newly Built   49835 non-null  int64 \n",
      " 3   Furnished     49835 non-null  int64 \n",
      " 4   Bedrooms      49835 non-null  int64 \n",
      " 5   Bathrooms     49835 non-null  int64 \n",
      " 6   Toilets       49835 non-null  int64 \n",
      " 7   Neighborhood  49835 non-null  object\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5742cc97",
   "metadata": {},
   "source": [
    "## 4.0 Data Modelling\n",
    "\n",
    "To make predictions on our data, we will be using various regression models from Scikit-Learn. Our goal is to predict the rent prices of various houses, given the combination of several variables. For this analysis, we will evaluate our model using the R2_score. A higher R2_score indicates that our model is performing well. Ultimately, we want to achieve a model that will give us more accurate predictions.\n",
    "\n",
    "Here are the regression models we will be using for our analysis:\n",
    "\n",
    "- Linear Regression\n",
    "- Ridge Regressor\n",
    "- Lasso Regressor\n",
    "- Random forest Regressor\n",
    "- Gradient Boost Regressor\n",
    "- Decision Tree Regressor\n",
    "\n",
    "### 4.1 Pre-processing\n",
    "\n",
    "- Importing important libraries\n",
    "- Spliting the data into dependent and independent variables\n",
    "- Splting the data into test and train dataset\n",
    "- Defining the column transformer, and the standardscaler.\n",
    "\n",
    "#### 4.1.1 Importing important libraries/models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1c49e571",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries for data splitting and preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Import specific regression models\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "# Import evaluation metrics\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Import pickle for saving and loading models\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58dcb891",
   "metadata": {},
   "source": [
    "#### 4.1.2 Splitting the data into dependent and independent variables\n",
    "\n",
    "Here, Price is our dependent variable(Target), while everyother columns are the dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6a102f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49835,) (49835, 7)\n"
     ]
    }
   ],
   "source": [
    "# Seperating the data into independent(x) and dependent(y) variables\n",
    "x = df.drop(columns= ['Price'])\n",
    "y = df['Price']\n",
    "\n",
    "print(y.shape, x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a521c1a6",
   "metadata": {},
   "source": [
    "#### 4.1.3 Splitting the dataset into train and test data.\n",
    "\n",
    "Here, 80% of our data will be our train data, and 20% will be the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0df5ad81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_Training set :  (39868, 7)\n",
      "Shape of x_Test set :  (9967, 7)\n",
      "Shape of y_Training set :  (39868,)\n",
      "Shape of y_Test set :  (9967,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting data in train and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state= 1)\n",
    "\n",
    "#Printing the shape of both the training and testing set to see how they've been split\n",
    "print(\"Shape of x_Training set : \", x_train.shape)\n",
    "print(\"Shape of x_Test set : \", x_test.shape)\n",
    "print(\"Shape of y_Training set : \", y_train.shape)\n",
    "print(\"Shape of y_Test set : \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b73775b",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "The train data has a total of 39,868 rows and the the test data has a total of 9,967 rows. \n",
    "\n",
    "\n",
    "#### 4.1.4 Hotencoding and Column Standardizing\n",
    "The models can only read binary numbers, so it is important that the Neighborhood column is converted to string to binaries, this is achievable using the OneHotEncoder library. Also, for our numeric columns, we would be standardizing the numeric columns in our x_test data to ensure that they are of the same scale, this helps the model in predicting better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "90fe40f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hot encoding the location column, to make them binary, thus machine readable\n",
    "column_trans = make_column_transformer((OneHotEncoder(sparse= False),['Neighborhood']),remainder='passthrough')\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c868429",
   "metadata": {},
   "source": [
    "#### 4.1.5  LinearRegression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f78d78ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score: 0.6665830021644317\n"
     ]
    }
   ],
   "source": [
    "# Create the LinearRegression\n",
    "lr = LinearRegression(n_jobs=-1)\n",
    "\n",
    "# Create the pipeline with column transformer, scaler, and the RandomForestRegressor\n",
    "pipe = make_pipeline(column_trans, scaler,lr)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipe.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "lr_y_pred = pipe.predict(x_test)\n",
    "\n",
    "# Calculate the evaluation\n",
    "r2 = r2_score(y_test, lr_y_pred)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"R-squared score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7d808e",
   "metadata": {},
   "source": [
    "#### 4.1.6  Lasso Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "88b26ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score: 0.6665745489449153\n"
     ]
    }
   ],
   "source": [
    "# Create the LASSO\n",
    "la = Lasso()\n",
    "\n",
    "# Create the pipeline with column transformer, scaler, and the Lasso Regressor\n",
    "pipe = make_pipeline(column_trans, scaler, la)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipe.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "la_y_pred = pipe.predict(x_test)\n",
    "\n",
    "# Calculate the R-squared score\n",
    "r2 = r2_score(y_test, la_y_pred)\n",
    "\n",
    "# Print the R-squared score\n",
    "print(\"R-squared score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aee3b7c",
   "metadata": {},
   "source": [
    "#### 4.1.7 GradientBoostingRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "73fea39a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score: 0.7116069766645055\n"
     ]
    }
   ],
   "source": [
    "# Create the GradientBoostingRegressor\n",
    "gb = GradientBoostingRegressor()\n",
    "\n",
    "# Create the pipeline with column transformer, scaler, and the GradientBoostingRegressor\n",
    "pipe = make_pipeline(column_trans, scaler, gb)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipe.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "gb_y_pred = pipe.predict(x_test)\n",
    "\n",
    "# Calculate the R-squared score\n",
    "r2 = r2_score(y_test, gb_y_pred)\n",
    "\n",
    "# Print the R-squared score\n",
    "print(\"R-squared score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e2e9ce",
   "metadata": {},
   "source": [
    "#### 4.1.8 RandomForestRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e0fc9b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score: 0.7439503655113542\n"
     ]
    }
   ],
   "source": [
    "# Create the RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Create the pipeline with column transformer, scaler, and the RandomForestRegressor\n",
    "pipe = make_pipeline(column_trans, scaler, rf)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipe.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "rf_y_pred = pipe.predict(x_test)\n",
    "\n",
    "# Calculate the R-squared score\n",
    "r2 = r2_score(y_test, rf_y_pred)\n",
    "\n",
    "# Print the R-squared score\n",
    "print(\"R-squared score:\", r2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2c8d89",
   "metadata": {},
   "source": [
    "#### 4.1.9  DecisionTreeRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "45630316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score: 0.7303962546562266\n"
     ]
    }
   ],
   "source": [
    "DC=DecisionTreeRegressor()\n",
    "\n",
    "# Create the pipeline with column transformer, scaler, and the DecisionTreeRegressor\n",
    "pipe = make_pipeline(column_trans, scaler, DC)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipe.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "DC_y_pred = pipe.predict(x_test)\n",
    "\n",
    "# Calculate the R-squared score\n",
    "r2 = r2_score(y_test, DC_y_pred)\n",
    "\n",
    "# Print the R-squared score\n",
    "print(\"R-squared score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1a255c",
   "metadata": {},
   "source": [
    "#### 4.1.10  XGBRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e19c843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score: 0.75256839151081\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# Create the Xgboosregressor model\n",
    "xgb = xgb.XGBRegressor()\n",
    "\n",
    "# Create the pipeline with column transformer, scaler, and the XGBRegressor\n",
    "pipe = make_pipeline(column_trans, scaler,xgb)\n",
    "\n",
    "# Fit the pipeline to the training data\n",
    "pipe.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "xgb_y_pred = pipe.predict(x_test)\n",
    "\n",
    "# Calculate the evaluation\n",
    "r2 = r2_score(y_test, xgb_y_pred)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"R-squared score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd382a13",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "The XGBRegressor model gives a better R2_score(75%) than the rest of the model, hence, it is our choice model for making the prediction. We will now proceed to saving the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6b0752d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully.\n"
     ]
    }
   ],
   "source": [
    "#Saving the model\n",
    "import pickle\n",
    "pickle.dump(pipe,open('xgb_model2.pkl','wb'))\n",
    "print(\"Model saved successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1eab52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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