EDA_with_Car_dataset.ipynb

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      "name": "EDA with Car dataset.ipynb",
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        "<a href=\"https://colab.research.google.com/github/ali-unlu/EDA-Car-Dataset/blob/main/EDA_with_Car_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c15f4606"
      },
      "source": [
        "# EDA for Automobile Dataset\n",
        "\n",
        "In this analysis, I will  demonstrate how to explore the cars.csv dataset using Python. I will first load the dataset and then process the data. I will also be visualizing the dataset and then finally apply ttest to explore the relationship between variables. When the final dataset is prepared, the same dataset can be used to develop various models. \n",
        "\n",
        "Data could be reached from [Kaggle with this link](https://www.kaggle.com/ljanjughazyan/cars1)"
      ],
      "id": "c15f4606"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72e1bce9"
      },
      "source": [
        "# required packages\n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt\n",
        "sns.set(color_codes=True)"
      ],
      "id": "72e1bce9",
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "21cebcf9",
        "outputId": "b9027509-8414-4aec-a011-2027495bef01"
      },
      "source": [
        "# data\n",
        "df = pd.read_csv('CARS.csv')\n",
        "df.head()"
      ],
      "id": "21cebcf9",
      "execution_count": null,
      "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Origin</th>\n",
              "      <th>DriveTrain</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>Invoice</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>MDX</td>\n",
              "      <td>SUV</td>\n",
              "      <td>Asia</td>\n",
              "      <td>All</td>\n",
              "      <td>$36,945</td>\n",
              "      <td>$33,337</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>265</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>4451</td>\n",
              "      <td>106</td>\n",
              "      <td>189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acura</td>\n",
              "      <td>RSX Type S 2dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$23,820</td>\n",
              "      <td>$21,761</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>2778</td>\n",
              "      <td>101</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acura</td>\n",
              "      <td>TSX 4dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$26,990</td>\n",
              "      <td>$24,647</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>3230</td>\n",
              "      <td>105</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acura</td>\n",
              "      <td>TL 4dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$33,195</td>\n",
              "      <td>$30,299</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>270</td>\n",
              "      <td>20</td>\n",
              "      <td>28</td>\n",
              "      <td>3575</td>\n",
              "      <td>108</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acura</td>\n",
              "      <td>3.5 RL 4dr</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Front</td>\n",
              "      <td>$43,755</td>\n",
              "      <td>$39,014</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>225</td>\n",
              "      <td>18</td>\n",
              "      <td>24</td>\n",
              "      <td>3880</td>\n",
              "      <td>115</td>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Make           Model   Type Origin DriveTrain     MSRP  Invoice  \\\n",
              "0  Acura             MDX    SUV   Asia        All  $36,945  $33,337   \n",
              "1  Acura  RSX Type S 2dr  Sedan   Asia      Front  $23,820  $21,761   \n",
              "2  Acura         TSX 4dr  Sedan   Asia      Front  $26,990  $24,647   \n",
              "3  Acura          TL 4dr  Sedan   Asia      Front  $33,195  $30,299   \n",
              "4  Acura      3.5 RL 4dr  Sedan   Asia      Front  $43,755  $39,014   \n",
              "\n",
              "   EngineSize  Cylinders  Horsepower  MPG_City  MPG_Highway  Weight  \\\n",
              "0         3.5        6.0         265        17           23    4451   \n",
              "1         2.0        4.0         200        24           31    2778   \n",
              "2         2.4        4.0         200        22           29    3230   \n",
              "3         3.2        6.0         270        20           28    3575   \n",
              "4         3.5        6.0         225        18           24    3880   \n",
              "\n",
              "   Wheelbase  Length  \n",
              "0        106     189  \n",
              "1        101     172  \n",
              "2        105     183  \n",
              "3        108     186  \n",
              "4        115     197  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "578ff260",
        "outputId": "35fd6365-533b-429f-d7d5-c588c22e60e0"
      },
      "source": [
        "# dimension of the df\n",
        "print(df.shape)\n",
        "\n",
        "# data types\n",
        "print(df.dtypes)"
      ],
      "id": "578ff260",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(428, 15)\n",
            "Make            object\n",
            "Model           object\n",
            "Type            object\n",
            "Origin          object\n",
            "DriveTrain      object\n",
            "MSRP            object\n",
            "Invoice         object\n",
            "EngineSize     float64\n",
            "Cylinders      float64\n",
            "Horsepower       int64\n",
            "MPG_City         int64\n",
            "MPG_Highway      int64\n",
            "Weight           int64\n",
            "Wheelbase        int64\n",
            "Length           int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ac959fb"
      },
      "source": [
        "We have now 428 rows and 15 columns.  While 7 of the variables are characters, the rest are integer or float but MSRP and invoice columns were set to characters, which need to convert into integer. Car price is our outcome or target variable and the rest will be used as the predictors of car price. "
      ],
      "id": "0ac959fb"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09127008"
      },
      "source": [
        "# Data processing"
      ],
      "id": "09127008"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09d14b84"
      },
      "source": [
        "Since car price, __MSRP__ , is our target variable and it is in __chr__ format, we need to first convert it to integers. Nevertheless, price has __$__ and __(,)__ signs, we first need to remove those and then convert it to real numbers."
      ],
      "id": "09d14b84"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7ebd83a"
      },
      "source": [
        "df['MSRP']= df.MSRP.apply(lambda x: x.replace('$', ''))\n",
        "df['MSRP']= df.MSRP.apply(lambda x: x.replace(',', ''))"
      ],
      "id": "e7ebd83a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3f0a5dbc",
        "outputId": "b3f3d90e-ebd5-4900-eb7c-00eb61163a34"
      },
      "source": [
        "df['MSRP']=pd.to_numeric(df['MSRP'],errors='coerce')\n",
        "df['MSRP'].head()"
      ],
      "id": "3f0a5dbc",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    36945\n",
              "1    23820\n",
              "2    26990\n",
              "3    33195\n",
              "4    43755\n",
              "Name: MSRP, dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2376c7ad"
      },
      "source": [
        "After converting price into numbers, we need to check the outliers of price since we use ttest to compare different groups. If there are some outliners in prices, it will affect  our results because ttest is mainly based on mean comparison. In that situation, an outlier will affect the groups mean. So, we will use normalization procedure. "
      ],
      "id": "2376c7ad"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3329e338",
        "outputId": "7ca73f21-d866-4331-81f7-b19ac369d06d"
      },
      "source": [
        "#Check the distribution\n",
        "sns.boxplot(x=df['MSRP'])"
      ],
      "id": "3329e338",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='MSRP'>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "923b8466",
        "outputId": "0eed3306-59a7-4ea5-86f9-13dc8928b93e"
      },
      "source": [
        "# let's see what kind of cars have extreme prices\n",
        "outliers = df[(df.MSRP > 75000)]\n",
        "print('Number of the cars in outlier list: ', len(outliers))\n",
        "\n",
        "print(outliers[['Make', 'Model', 'MSRP']])\n"
      ],
      "id": "923b8466",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of the cars in outlier list:  17\n",
            "              Make                                Model    MSRP\n",
            "6            Acura               NSX coupe 2dr manual S   89765\n",
            "20            Audi                             RS 6 4dr   84600\n",
            "61        Cadillac                  XLR convertible 2dr   76200\n",
            "114          Dodge         Viper SRT-10 convertible 2dr   81795\n",
            "199         Jaguar                        XKR coupe 2dr   81995\n",
            "200         Jaguar                  XKR convertible 2dr   86995\n",
            "251  Mercedes-Benz                                 G500   76870\n",
            "261  Mercedes-Benz                            CL500 2dr   94820\n",
            "262  Mercedes-Benz                            CL600 2dr  128420\n",
            "268  Mercedes-Benz                             S500 4dr   86970\n",
            "269  Mercedes-Benz                SL500 convertible 2dr   90520\n",
            "270  Mercedes-Benz                         SL55 AMG 2dr  121770\n",
            "271  Mercedes-Benz                SL600 convertible 2dr  126670\n",
            "331        Porsche  911 Carrera convertible 2dr (coupe)   79165\n",
            "332        Porsche   911 Carrera 4S coupe 2dr (convert)   84165\n",
            "333        Porsche                  911 Targa coupe 2dr   76765\n",
            "334        Porsche                          911 GT2 2dr  192465\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab75e417"
      },
      "source": [
        "In our data set, there are 17 cars can be categorized as outliers and all these cars in in luxury category. Depending on the research aims, these can be kept as is in the dataset but since we will make ttest, these cars absoulately affect our result, so we will remove them. "
      ],
      "id": "ab75e417"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cd4fea99"
      },
      "source": [
        "### Outliers\n",
        "\n",
        "As seen above, there are few prices that are outliers. To normalize our outcome variable, I will use interquartile range technique.\n",
        "\n",
        "__According to Wikipedia:__ The interquartile range (IQR), also called the midspread or middle 50%, or technically H-spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles, IQR = Q3 − Q1."
      ],
      "id": "cd4fea99"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ca5ea61",
        "outputId": "ecf40f00-e955-4e28-899f-e7d5a86b8b97"
      },
      "source": [
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "id": "1ca5ea61",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MSRP           18870.750\n",
            "EngineSize         1.525\n",
            "Cylinders          2.000\n",
            "Horsepower        90.000\n",
            "MPG_City           4.250\n",
            "MPG_Highway        5.000\n",
            "Weight           873.750\n",
            "Wheelbase          9.000\n",
            "Length            16.000\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4e1e5f21",
        "outputId": "1f821907-55cd-4c00-e77c-8187f650f0d6"
      },
      "source": [
        "df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "id": "4e1e5f21",
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\aunc\\AppData\\Local\\Temp/ipykernel_3960/2227700030.py:1: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version.  Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n",
            "  df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9a5fdcb9",
        "outputId": "6ba5df1e-281c-45d9-b9c8-1a65dcc39213"
      },
      "source": [
        "# re-checked the distribution\n",
        "sns.boxplot(x=df['MSRP'])"
      ],
      "id": "9a5fdcb9",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='MSRP'>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b18eb499"
      },
      "source": [
        "After using the technique now as seen below the MSRP box plot contains no outlier points this is a big improvement. Previously there were over 15 points of outliers now I have removed those outliers"
      ],
      "id": "b18eb499"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7905130f"
      },
      "source": [
        "\n",
        "df['Invoice']= df.Invoice.apply(lambda x: x.replace('$', ''))\n",
        "df['Invoice']= df.Invoice.apply(lambda x: x.replace(',', ''))\n"
      ],
      "id": "7905130f",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80103310",
        "outputId": "26b19eee-d0d5-475e-d375-f4e3fc028c18"
      },
      "source": [
        "df['Invoice']=pd.to_numeric(df['Invoice'],errors='coerce')\n",
        "df['Invoice']"
      ],
      "id": "80103310",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      33337\n",
              "1      21761\n",
              "2      24647\n",
              "3      30299\n",
              "4      39014\n",
              "       ...  \n",
              "423    38203\n",
              "424    40083\n",
              "425    42573\n",
              "426    24641\n",
              "427    33112\n",
              "Name: Invoice, Length: 341, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0886439d"
      },
      "source": [
        " I also test the Robust scaleer function to the target variable to see how it affects its distribution. The process converts the values between -1 to +2.5 values and it can be used if the model will be used for ML models but since we will make a ttest, this process does not fit well to our model. I kept the data as processed above. This codes are only for my future references. "
      ],
      "id": "0886439d"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73f5b17f"
      },
      "source": [
        "\n",
        "from sklearn import preprocessing\n",
        "robust_scaler = preprocessing.RobustScaler()\n",
        "\n",
        "column_names_to_normalize = ['MSRP', 'Invoice']\n",
        "x = df[column_names_to_normalize].values\n",
        "\n",
        "# x_scaled = min_max_scaler.fit_transform(x)\n",
        "x_scaled = robust_scaler.fit_transform(x)\n",
        "\n",
        "\n",
        "df_temp = pd.DataFrame(x_scaled, columns=column_names_to_normalize, index = df.index)\n",
        "df[column_names_to_normalize] = df_temp\n",
        "\n",
        "\n"
      ],
      "id": "73f5b17f",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2d232807"
      },
      "source": [
        "# re-checked \n",
        "#Check the distribution\n",
        "sns.boxplot(x=df['MSRP'])"
      ],
      "id": "2d232807",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10816bea",
        "outputId": "d934ada3-a42f-4ebe-9c48-21ddf9e82ff8"
      },
      "source": [
        "# summary of the df\n",
        "df.describe()"
      ],
      "id": "10816bea",
      "execution_count": null,
      "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>MSRP</th>\n",
              "      <th>Invoice</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>MPG_City</th>\n",
              "      <th>MPG_Highway</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>339.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>341.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29789.439883</td>\n",
              "      <td>27332.146628</td>\n",
              "      <td>3.106158</td>\n",
              "      <td>5.684366</td>\n",
              "      <td>210.513196</td>\n",
              "      <td>19.624633</td>\n",
              "      <td>26.618768</td>\n",
              "      <td>3543.299120</td>\n",
              "      <td>107.803519</td>\n",
              "      <td>186.304985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11048.748802</td>\n",
              "      <td>9961.342314</td>\n",
              "      <td>0.890269</td>\n",
              "      <td>1.320332</td>\n",
              "      <td>54.939839</td>\n",
              "      <td>2.928538</td>\n",
              "      <td>3.834994</td>\n",
              "      <td>562.054298</td>\n",
              "      <td>5.905091</td>\n",
              "      <td>11.652099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10280.000000</td>\n",
              "      <td>9875.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>2403.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>158.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21445.000000</td>\n",
              "      <td>19801.000000</td>\n",
              "      <td>2.400000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>3188.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>178.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27560.000000</td>\n",
              "      <td>25218.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>3470.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>187.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36395.000000</td>\n",
              "      <td>33129.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>3851.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>193.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>65000.000000</td>\n",
              "      <td>59912.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>390.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>5270.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>215.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               MSRP       Invoice  EngineSize   Cylinders  Horsepower  \\\n",
              "count    341.000000    341.000000  341.000000  339.000000  341.000000   \n",
              "mean   29789.439883  27332.146628    3.106158    5.684366  210.513196   \n",
              "std    11048.748802   9961.342314    0.890269    1.320332   54.939839   \n",
              "min    10280.000000   9875.000000    1.300000    4.000000  104.000000   \n",
              "25%    21445.000000  19801.000000    2.400000    4.000000  170.000000   \n",
              "50%    27560.000000  25218.000000    3.000000    6.000000  208.000000   \n",
              "75%    36395.000000  33129.000000    3.500000    6.000000  240.000000   \n",
              "max    65000.000000  59912.000000    5.700000    8.000000  390.000000   \n",
              "\n",
              "         MPG_City  MPG_Highway       Weight   Wheelbase      Length  \n",
              "count  341.000000   341.000000   341.000000  341.000000  341.000000  \n",
              "mean    19.624633    26.618768  3543.299120  107.803519  186.304985  \n",
              "std      2.928538     3.834994   562.054298    5.905091   11.652099  \n",
              "min     13.000000    17.000000  2403.000000   95.000000  158.000000  \n",
              "25%     18.000000    25.000000  3188.000000  104.000000  178.000000  \n",
              "50%     19.000000    26.000000  3470.000000  107.000000  187.000000  \n",
              "75%     21.000000    29.000000  3851.000000  112.000000  193.000000  \n",
              "max     27.000000    36.000000  5270.000000  124.000000  215.000000  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffd93252"
      },
      "source": [
        "Interestingle enough that the data has few missing variables. It seems only cylinders column has two missing variables. We can replace missing value with median. "
      ],
      "id": "ffd93252"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "959d0fd2",
        "outputId": "da4502df-0fd3-4209-d31f-8380199a9180"
      },
      "source": [
        "# remove dublicates\n",
        "df = df.drop_duplicates(subset='MSRP', keep='first')\n",
        "df.count()\n"
      ],
      "id": "959d0fd2",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Make           327\n",
              "Model          327\n",
              "Type           327\n",
              "Origin         327\n",
              "DriveTrain     327\n",
              "MSRP           327\n",
              "Invoice        327\n",
              "EngineSize     327\n",
              "Cylinders      325\n",
              "Horsepower     327\n",
              "MPG_City       327\n",
              "MPG_Highway    327\n",
              "Weight         327\n",
              "Wheelbase      327\n",
              "Length         327\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bb09192",
        "outputId": "7f81319c-f2cc-48f4-ff2a-aaf060c4085e"
      },
      "source": [
        "# replace missing with median because it is a cyclinder\n",
        "\n",
        "df['Cylinders'] = df['Cylinders'].fillna(df['Cylinders'].median())\n",
        "df.count()\n"
      ],
      "id": "5bb09192",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Make           327\n",
              "Model          327\n",
              "Type           327\n",
              "Origin         327\n",
              "DriveTrain     327\n",
              "MSRP           327\n",
              "Invoice        327\n",
              "EngineSize     327\n",
              "Cylinders      327\n",
              "Horsepower     327\n",
              "MPG_City       327\n",
              "MPG_Highway    327\n",
              "Weight         327\n",
              "Wheelbase      327\n",
              "Length         327\n",
              "dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16642843"
      },
      "source": [
        "# Plots"
      ],
      "id": "16642843"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1b78173e"
      },
      "source": [
        "We can look at how many different cars companies have with bar plot. I will include the top 40 companies according to their repository. "
      ],
      "id": "1b78173e"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4c28eb7",
        "outputId": "c65df804-833f-43c1-a9dd-64207145c34a"
      },
      "source": [
        "# Plotting a Histogram\n",
        "df.Make.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5))\n",
        "plt.title(\"Number of cars by make\")\n",
        "plt.ylabel('Number of cars')\n",
        "plt.xlabel('Make');"
      ],
      "id": "c4c28eb7",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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19c3vZhIREREVqHxJ0o4dO6b8uX79+tixY0em5/j4+Ch/trS0REBAQH40jYiIiEgvcccBIiIiIj3EJI2IiIhIDzFJIyIiItJDTNKIiIiI9BCTNCIiIiI9xCSNiIiISA8xSSMiIiLSQ0zSiIiIiPQQkzQiIiIiPcQkjYiIiEgPMUkjIiIi0kNM0oiIiIj0EJM0IiIiIj3EJI2IiIhIDxkVdAMob5X6ojiKFVX9Zy5XrpTy5+T3aYh/m5TfzSIiIiI1mKR94ooVNYL92L3ZPh68wBHx+dgeIiIikobDnURERER6iEkaERERkR5ikkZERESkh5ikEREREekhJmlEREREeohJGhEREZEeYpJGREREpIeYpBERERHpoTxP0hISEtClSxc8efIEAODp6YlOnTrB0dERjo6OOHz4cKaY27dvo2vXrrC2tsaUKVOQlpaW180kIiIi0it5mqRdu3YNvXv3xsOHD5X3hYWFYdOmTdi7dy/27t2Ljh07ZoobP348pk6dioMHD0IIgR07duRlM4mIiIj0Tp4maTt27IC3tzfMzMwAAO/evUNUVBSmTp0Ke3t7LF26FHK5XCXm6dOnSE5ORsOGDQEALi4uOHDgQF42k4iIiEjv5OnenT4+Piq3Y2Nj0axZM8ycORMlSpTAkCFDsGvXLvTo0UP5nOjoaJQrV055u1y5cnjx4oVG5y1btqRGz8+44bg2PuX4lNR0GBcxzPb5WT2uy/kYn7fxhbntjGc84wtvfGFue0HG5+sG6xUrVsTy5cuVt93c3BAYGKiSpAkhMsXJZDKNzhMbmwC5/MNxpPxhYmKy32Kc8aXUbtCeU3xWx9Pk+YzPvfjC3HbGM57xhTe+MLc9r+MNDGQ5dizl6+rO8PBwHDx4UHlbCAEjI9U80dzcHC9fvlTejomJUQ6XEhEREX0u8jVJE0Jgzpw5iIuLQ2pqKrZv355p4UCFChVQtGhRXLp0CQAQGBiI1q1b52cziYiIiApcviZptWvXxuDBg9G7d2/Y2dmhTp066NKlCwBg0KBBuHHjBgDA398fc+fOha2tLZKSktCvX7/8bCYRERFRgcuXOWnHjh1T/uzq6gpXV9dMz1m7dq3y59q1a2PXrl350TQiIiIivcQdB4iIiIj0EJM0IiIiIj3EJI2IiIhIDzFJIyIiItJDTNKIiIiI9BCTNCIiIiI9xCSNiIiISA/l696d9Pkp9UVxFCuq+jLLuJ9o8vs0xL9Nyu9mERER6T0maZSnihU1UrtBu/bb1hIREX26ONxJREREpIeYpBERERHpISZpRERERHqISRoRERGRHlKbpCUlJeHq1asAgD///BOenp6IiorK63YRERERfdbUJmmenp44evQorl+/jv/+978oX748pk6dmh9tIyIiIvpsqU3SHj9+jLFjx+L48eNwdnaGu7s73rx5kw9NIyIiIvp8qU3SUlNTAQCnT59Gs2bNkJ6ejnfv3uV5w4iIiIg+Z2qL2VpYWKBz584wNDREo0aN8NNPP6FFixb50TYiIiKiz5baJG348OF4/PgxatWqBQMDA/z8889o3bp1frSNiIiI6LOldrjzp59+gqWlJUqV+rDfYtu2bWFgwModRERERHlJbU9ahQoVcPnyZTRs2JDJGeU7XTdo5wbvRERUWKlN0h48eIA+ffrAyMgIxsbGEEJAJpPh8uXL+dE++szpukE7N3gnIqLCSm2Stnnz5vxoBxERERFloHb8skKFCoiLi8OzZ88QFRWFx48f459//pF8goSEBHTp0gVPnjwBAGzfvh1dunSBvb09PD09kZKSkikmMDAQrVq1gqOjIxwdHbFo0SINfiUiIiKiwk9tT5qXlxeOHj2K5ORkmJubIzIyEv/5z3/Qo0cPtQe/du0avLy88PDhQwBAREQE1q9fjz179sDExASTJk3Cli1b0L9/f5W4GzduYNKkSejSpYtWvxQRERFRYae2Jy00NBRHjx5Fp06dsGbNGmzYsAHFihWTdPAdO3bA29sbZmZmAABjY2NMnz4dJUuWhEwmQ82aNbPcB/TGjRsIDAyEg4MDxo0bh7i4OA1/LSIiIqLCTW1PWrly5VCiRAlUrVoVd+/eRYcOHeDr6yvp4D4+Piq3K1SogAoVKgAAXr16hc2bN2Pu3LlZnnPw4MGoX78+Fi5ciJkzZ2LBggWSzgkAZcuWlPzcD+crpf5JjGd8AZ0vN+MLc9sZz3jGF974wtz2goxXm6QVKVIEFy5cQLVq1XDq1Ck0bdoUr1+/1upkCi9evMAvv/yCrl27omnTppkeX758ufLnX375BR06dNDo+LGxCZDLBQBpf5iYmOzX9zH+847P6niaPF+f4gtz2xnPeMYX3vjC3Pa8jjcwkOXYsaR2uHPcuHHYtm0b2rRpg9u3b6NZs2ZwcHDQurEPHjxA79694ezsjBEjRmR6PD4+Hhs2bFDeFkLAyEhtLklERET0SVGb/TRs2BBly5ZFkSJF8Mcff+DRo0eoW7euVidLSEjAzz//DA8PDzg6Omb5nBIlSmDdunWwsLBAgwYNsGnTJnTs2FGr8xEREREVVmp70jZu3Ijhw4cDAF6/fo1Ro0Zh586dWp1s165dePnyJX7//XdleY0lS5YAAKZMmYKjR4/C0NAQixcvxvTp02Fra4ubN29i/PjxWp2PiIiIqLBS25O2fft2bNu2DQBQsWJFBAYGok+fPujevbvkkxw7dgwA0L9//0zlNhQyLjKwtLREQECA5OMTERERfWrU9qSlp6ejZMn/TWorVaoUZDJZnjaKiIiI6HOntietatWq8Pf3R8+ePQEAe/bsQeXKlfO6XUR6gRu8ExFRQVGbpM2YMQMzZsyAk5MTjIyM0KJFC0yfPj0fmkZU8LjBOxERFRS1SdpXX32F3377LT/aQkRERET/T+2cNCIiIiLKf0zSiIiIiPRQtkna4cOHAQApKSn51hgiIiIi+iDbJG3p0qUAoFzVSURERET5J9uFAyYmJrC2tsaLFy9gb2+f6fHg4OA8bRgRERHR5yzbJG3dunW4ffs2pkyZgqlTp+Znm4iIiIg+e9kmaSVLlkTjxo2xevVqmJmZ4ebNm0hLS0P9+vVVdiAgIiIiotyntk5afHw83Nzc8NVXXyE9PR0vXrzAqlWr0KhRo/xoHxEREdFnSW2SNn/+fPj7+6NZs2YAgDNnzmDevHnYsWNHnjeOiIiI6HOltk5aQkKCMkEDgObNmyMpiXsNEhEREeUltT1pBgYGePr0KSpUqAAAePLkCQwNDfO8YUTEDdqJiD5napO0ESNGoGfPnmjevDkA4J9//oG3t3eeN4yIuEE7EdHnTG2S1qFDB1StWhVnz56FEAJDhw5FtWrV8qNtRERERJ8ttUkaAFStWhVVq1bN67YQERER0f/jButEREREeohJGhEREZEeUpukTZgwIT/aQUREREQZqE3S7ty5AyFEfrSFiIiIiP6f2oUD5cqVg52dHRo0aAATExPl/V5eXnnaMCIiIqLPmdokzcLCAhYWFvnRFiIiIiL6f2qTtJEjRyI5ORmPHj1CjRo1kJKSgmLFikk6eEJCAnr16oVVq1bh22+/RWhoKObOnYv379/D1tYWHh4emWKioqIwfvx4xMbGokqVKvD391fpwSMiIiL6HKidk3bt2jV06NABQ4YMQXR0NNq0aYPLly+rPfC1a9fQu3dvPHz4EACQnJyMyZMnY8WKFQgJCUFYWBhOnjyZKW7GjBno06cPDhw4gHr16mHFihWa/1ZEREREhZzaJG3+/PnYsGEDSpcuja+//hq+vr7w8fFRe+AdO3bA29sbZmZmAIDr16+jUqVKqFixIoyMjGBvb48DBw6oxKSmpuLChQuwtrYGALi4uGR6DhEREdHnQO1wZ3JyMqpXr6683aZNGyxatEjtgT9O5KKjo1GuXDnlbTMzM7x48ULlOa9fv0bJkiVhZPShWeXKlcv0HCnKli2p0fMzblitDcYzXl/jU1LTYVzEMMvnfvyYulhN4zVtK+MZz/hPN74wt70g49UmaUZGRoiLi4NMJgMA/Pvvv1qdKKsyHopjavIcKWJjEyCXfziWlD9MTEz2W1QznvGFPT67DdqDFzhqHSslPqvjafJ8xjOe8Z9GfGFue17HGxjIcuxYUpukDRs2DH379kVMTAx+/fVX/PPPP5g5c6bGjTQ3N8fLly+Vt6Ojo5VDoQqmpqZISEhAeno6DA0NERMTk+k5RERERJ8DtUmalZUVqlatin/++QdyuRzDhw9XGf6UqkGDBoiIiMCjR4/w7bffYt++fejatavKc4oUKQJLS0uEhITA3t4egYGBaN26tcbnIiIiIirsJO3dmZaWBrlcDiMjIxQpUkSrExUtWhTz5s2Du7s7OnfujKpVq8LGxgYAMGXKFBw9ehQA4O3tjR07dqBz5864ePEixowZo9X5iIiIiAoztT1pu3fvxoIFC/Djjz9CLpdj2bJlmDp1qnIFpjrHjh1T/ty8eXMEBQVlek7GRQYVKlTAxo0bJR2biIiI6FOlNknbsGEDAgMDlXPDoqKiMGTIEMlJGhERERFpTu1wZ5EiRVQm75cvX17rIU8iIiIikibbnrSbN28CAGrVqoWZM2eiZ8+eMDQ0xJ49e9CoUaN8ayARERHR5yjbJM3d3V3l9okTJ5Q/y2QyeHl55VmjiIiIiD532SZpGSf8ExEREVH+UrtwICYmBgEBAXjz5o3K/RMmTMirNhERERF99tQuHBg2bBiuX78OIYTKf0RERESUd9T2pKWmpmLZsmX50RYi+oSU+qI4ihVVvcRk3Is0+X0a4t8mfbLxRES6Upuk1a1bF3fv3kXNmjXzoz1E9IkoVtRI7QbtOW1ZXNjjiYh0pTZJa9SoEZycnFCuXDkYGf3v6YptnIiIiIgo96lN0pYtWwZ/f3989913+dEeIiIiIoKEJO3LL79E586d86MtRERERPT/1CZpbdu2xfz589GpUycYGxsr769bt26eNoyIiIjoc6Y2SQsODgYAHDx4UHmfTCbjnDQiIiKiPKQ2SePOA0RERET5T22S9scff2R5/4ABA3K9MURERET0gdok7e7du8qfU1JScOnSJTRt2jRPG0VERET0uVObpM2dO1fl9qtXr7hvJxEREVEeU7t358dMTU3x9OnTvGgLEREREf0/jeakCSEQFhaGsmXL5mmjiIiIiD53Gs1JA4BvvvmGw51ERGoU9AbvBR1PRLrTeE4aERGpV9AbvBd0PBHpLtskzdPTM9sgmUyGOXPm5EmDiIiIiCiHJK1GjRqZ7nv9+jX+/PNPVKhQQesT7ty5E5s2bVLefvLkCRwdHTFt2jTlfcuWLcPu3bvxxRdfAAB69OgBV1dXrc9JREREVNhkm6QNHDhQ5XZoaCgmTpwIe3t7eHl5aX3C7t27o3v37gCAe/fuYcSIERg5cqTKc8LCwrBw4UJYWFhofR4iIiKiwkztnLS0tDQsWLAAAQEBmD59OmxsbHLt5NOnT4eHhwdMTU1V7g8LC8PatWvx+PFjNG7cGBMnTkTRokVz7bxERERE+i7HJO3Ro0fw8PBAiRIlEBAQgG+++SbXThwaGork5GTY2tqq3J+YmIg6depg4sSJqFChAiZNmoQVK1bAw8ND8rHLli2pUVsyrljSBuMZX1jjC3PbGV/44gtbexmfe/GFue0FGZ9tkrZr1y74+vpiwIABGDZsmNYNy862bduy3P/TxMQEa9euVd4eOHAgJk+erFGSFhubALlcAJD2h4mJyX6NEuMZ/ynHF+a2M16/47M6nibPZ/ynE1+Y257X8QYGshw7lrLdccDLywsJCQlYs2YNGjVqpPzPwsICjRo10rqxwIc9QC9cuIB27dpleiwqKgq7du1S3hZCwMhI7agsERER0Scl2+zn6NGjeXbS8PBwVK5cGSVKlMj0WLFixeDn54emTZvi22+/xebNm9GxY8c8awsRERGRPso2SdOlzIY6jx8/xtdff61y36BBgzBq1Cj88MMPmDlzJoYNG4bU1FQ0atQoy2FRIiIiok9ZgYwjdu7cGZ07d1a5L+M8NGtra1hbW+d3s4iIiIj0Bid7ERFRrivovUO59yh9CpikERFRrivovUO59yh9CrJd3UlEREREBYdJGhEREZEeYpJGREREpIeYpBERERHpISZpRERERHqISRoRERGRHmKSRkRERKSHmKQRERER6SEmaURERER6iEkaERERkR5ikkZERESkh7h3JxER0UcKeoN3bhBPAJM0IiKiTAp6g3duEE8AhzuJiIiI9BKTNCIiIiI9xCSNiIiISA8xSSMiIiLSQ0zSiIiIiPQQkzQiIiIiPcQkjYiIiEgPMUkjIiIi0kMFUsy2X79+iI2NhZHRh9PPnDkTDRo0UD4eGhqKuXPn4v3797C1tYWHh0dBNJOIiIiowOR7kiaEwL///osTJ04ok7SMkpOTMXnyZGzcuBHffPMNhgwZgpMnT6JNmzb53VQiIiKiApPvw53//vsvZDIZBg0aBAcHB2zatEnl8evXr6NSpUqoWLEijIyMYG9vjwMHDuR3M4mIiIgKVL73pL19+xbNmzfH9OnTkZycjH79+qFKlSpo2bIlACA6OhrlypVTPt/MzAwvXrzQ6Bxly5bU6PkZN63VBuMZX1jjC3PbGc94xmcfn5KaDuMihtk+P6vHdTlfXsZ+zvH5nqRZWFjAwsICAFCiRAl069YNJ0+eVCZpQohMMTKZTKNzxMYmQC7/cBwpf5iYmOy3qWU84z/l+MLcdsYznvE5x6vboD2n+KyOp8nzcyv2U483MJDl2LGU78OdFy9exJkzZ5S3hRAqc9PMzc3x8uVL5e3o6GiYmZnlaxuJiIiIClq+J2nx8fHw9fXF+/fvkZCQgICAAHTs2FH5eIMGDRAREYFHjx4hPT0d+/btQ+vWrfO7mUREREQFKt+HO62srHDt2jU4OTlBLpejT58+sLCwgKOjI9asWQNzc3PMmzcP7u7ueP/+Pdq0aQMbG5v8biYRERFRgSqQOmljxozBmDFjVO7bu/d/Y+fNmzdHUFBQPreKiIiISH9wxwEiIiIiPcQkjYiIiEgPMUkjIiIi0kNM0oiIiIj0EJM0IiIiIj3EJI2IiIhIDzFJIyIiItJDBVInjYiIiPRXqS+Ko1hR1RQh436iye/TEP82Kb+b9dlhkkZEREQqihU1UrtBu/ZbjpNUHO4kIiIi0kNM0oiIiIj0EJM0IiIiIj3EJI2IiIhIDzFJIyIiItJDTNKIiIiI9BCTNCIiIiI9xCSNiIiISA8xSSMiIiLSQ0zSiIiIiPQQkzQiIiIiPcS9O4mIiCjX6Lo5Ozd3/x8maURERJRrdN2cnZu7/w+HO4mIiIj0EJM0IiIiIj1UIMOdy5Ytw/79+wEAbdq0wYQJEzI9vnv3bnzxxRcAgB49esDV1TXf20lERERUUPI9SQsNDcXp06cREBAAmUyGX375BYcPH0bHjh2VzwkLC8PChQthYWGR380jIiIi0gv5nqSVK1cOkyZNgrGxMQCgWrVqiIqKUnlOWFgY1q5di8ePH6Nx48aYOHEiihYtmt9NJSIiIiow+Z6k1ahRQ/nzw4cPERISgm3btinvS0xMRJ06dTBx4kRUqFABkyZNwooVK+Dh4SH5HGXLltSoTRmX9mqD8YwvrPGFue2MZzzjC298fp+7sP6tCqwEx7179zBkyBBMnDgRlStXVt5vYmKCtWvXKm8PHDgQkydP1ihJi41NgFwuAEj7w8TEZL+Yl/GM/5TjC3PbGc94xutnfEG3PavjafL8/Iw3MJDl2LFUIKs7L126hP79+2Ps2LFwdnZWeSwqKgq7du1S3hZCwMiI5dyIiIjo85LvSdqzZ88wYsQI+Pv7w87OLtPjxYoVg5+fHx4/fgwhBDZv3qyyqICIiIjoc5DvXVTr16/H+/fvMW/ePOV9vXr1wrFjxzBq1Cj88MMPmDlzJoYNG4bU1FQ0atQIAwYMyO9mEhERERWofE/SvLy84OXllen+3r17K3+2traGtbV1fjaLiIiISK9wshcRERF9Mj6lDdqZpBEREdEn41PaoJ17dxIRERHpISZpRERERHqISRoRERGRHmKSRkRERKSHmKQRERER6SEmaURERER6iEkaERERkR5ikkZERESkh5ikEREREekhJmlEREREeohJGhEREZEe4t6dRERERP9P1w3ac3ODdyZpRERERP9P1w3ac3ODdw53EhEREekhJmlEREREeohJGhEREZEeYpJGREREpIeYpBERERHpISZpRERERHqISRoRERGRHmKSRkRERKSHCiRJCw4ORufOndGxY0ds3rw50+O3b99G165dYW1tjSlTpiAtLa0AWklERERUcPI9SXvx4gUWLVqELVu2YO/evdi+fTvu37+v8pzx48dj6tSpOHjwIIQQ2LFjR343k4iIiKhA5fu2UKGhoWjWrBlKly4NALC2tsaBAwcwcuRIAMDTp0+RnJyMhg0bAgBcXFywdOlS9OnTR/I5DAxkKrfNyhTX6PkfYzzjP9X4wtx2xjOe8fobX5jbnp/x6o4jE0KIHJ+Ry1avXo13797Bw8MDALBz505cv34ds2bNAgBcuXIFvr6+2Lp1KwDg0aNHGDx4MA4ePJifzSQiIiIqUPk+3JlVTiiTySQ/TkRERPQ5yPckzdzcHC9fvlTejo6OhpmZWbaPx8TEqDxORERE9DnI9yStRYsWOHPmDF69eoWkpCQcOnQIrVu3Vj5eoUIFFC1aFJcuXQIABAYGqjxORERE9DnI9zlpwIcSHKtXr0Zqaiq6deuGQYMGYdCgQRg1ahR++OEH3LlzB15eXkhMTMT333+PuXPnwtjYOL+bSURERFRgCiRJIyIiIqKccccBIiIiIj3EJI2IiIhIDzFJIyIiItJDTNKIiIiI9BCTNCIiIiI9xCSNiIgoD7x58wbPnj1DVFQUHj9+jDNnzhR0kygfDBw4MNeOle8brBdGcXFx+PLLL1Xue/r0KSpUqFBALQJSUlJyrB13/PhxWFlZITAwMMvHnZycJJ3n1atXMDU11aKFlFvevHmDpKQkCCGQnp6OJ0+eoHnz5gXdrM9CYmIiAgMD4erqihcvXmDbtm0YPHgwihfPefNk+p+EhATEx8erbPlXvnx5SbGxsbG4du0ajIyMUL9+fZQuXTqPWpmZp6cn5s6dq3X8ggULsGXLFqSlpaF06dKIjo5GvXr1sHPnTsnHGDRoEFxcXNChQwcUKVJE4zakpaXByMgIaWlpSElJQYkSJSTFpaenIyUlRfk6f/DgAb777jut2lCQbt++jbNnz8LQ0BAtW7ZEtWrV8uW8ycnJePbsGb755hudj/VJJ2nt2rXLcd/Po0eP5hj/7NkzCCEwePBgrF27VnmRSU9Px6BBg3DgwIEc493c3NCqVSu0aNECP/zwg+a/wP+zt7fHrFmz0LBhQ+V9PXv2REBAQLYxN27cgJWVFc6dO5fl41KTNFdXV+zfv1+T5qq4ePEi1q9fj3fv3kEIAblcjqioKBw7dkzyMW7duoVVq1YhLi5O5UL/3//+V23snj17MH/+fLx9+xbAh71hZTIZbt++rTY2txLdkydP4uzZs0hLS0PTpk3RoUMHSXGA7hf6f//9F1u2bFH5+z958gSbN2+WFP/mzRvcunULLVq0wOrVq3Hz5k2MGjUK1atXlxT/9OlTeHl54enTp9i0aRPGjRuHOXPm4Ntvv5UUf+zYMbRr1055Ozo6GrNmzcJvv/0mKb527dqZrgHlypXDqVOnJMWPHTsWtWrVAgCYmJhALpdjwoQJas//22+/wd3dHZ6enlk+ru7DPzdee3///TcWLVqEt2/fQgihfO2ru+5llJKSgpMnTyIxMREAlF8SRo8eLSl+1apVWLNmjUpyJbUN+/fvh4+PDxo1agS5XI5p06Zh5syZknaguXnzJurWrYsLFy5k+Xjjxo3VHuPu3btITEyEiYmJ2udm5a+//sLJkyfh4+ODYcOGISoqCn/88YdGxxg8eDACAgLg5+eHNm3awNnZGfXr15cUGxISgpUrVyI4OBhRUVHo27cvpk2bpvb68/jxY/z8888YN24cOnXqBAD4448/cOHCBaxfv17yezclJQXr169HREQEpk2bhg0bNmDw4MGSC9PHxcXBz88PkZGRWLJkCXx9fTFp0qRMHSbZWb9+PbZv34527dpBLpdj6NChGDp0KLp27SopXpf33uvXr9GuXTuULVsWRYsW1eq9p/BJJ2kbN26EEALLly9HxYoV4eLiAkNDQwQHB+PJkydq45cuXYpz584hOjoarq6uyvuNjIzQtm1btfFDhgzBxYsX4evri8jISFhYWKBly5Zo2bKl5G+SwIcX65QpU/DTTz+hR48eALLeiD6jUaNGAVD/YaBO7dq1ERgYiPr166NYsWLK+6W238vLC4MGDUJAQADc3Nxw6tQpfP/99xq1YeLEiejZsydq1KiRY9KdleXLl2Pjxo2oWbOmRnFA7iS6a9euxaFDh2Bvbw8hBFatWoX79+9j6NChktqg64Xew8MD7du3x6VLl+Ds7IxTp06hRo0akuPHjh0LKysrAMCBAwfw008/wdvbW3KSN23aNPz8889YsGABypUrhy5dumDixImS4xctWoT09HR07NgRmzdvxvLly1Xei+rcuXNH+XNqaiqOHDmCq1evSo6PiorCqlWrAAAlS5aEh4cHHB0d1cbVrVsXANCkSRPJ58ooN157s2fPxqRJk7R63yiMHDkSSUlJiIyMhKWlJS5cuKDyZVGdXbt24ciRI1r1xq9cuRJ79uxR7t389OlTDBs2TFKStnXrVsyePRtLly7N9JhMJpP0Bc/AwABWVlaoUqUKihYtqrxfSiwAmJmZoWTJkqhRowbu3LmDTp06wc/PT1KsQuPGjdG4cWMkJyfjwIEDGDVqFEqWLIlu3bqhT58+OSY8K1euVF4rvvvuOwQEBGDgwIFqkzQfHx+4u7srEzTgw2tp9+7dmDNnDlasWCGp7TNnzoSpqSlu3boFQ0NDREZGYsqUKZL/BlOnTkXLli1x/fp1mJiYwMzMDOPHj8eaNWskxe/YsQN79uxByZIlAQAjRoxA7969JSdpGd97qampuHTpEiwtLSW999atWyfpHJKIz4Czs7Ok+7KzevVqndvw/v17cfHiRbF69Wphb28vrK2tJcc6OTmJ169fi379+ompU6eK1NRU4eTklGOMlZWVaNeuXZb/tW/fXvK5raysMv3Xrl07yfGOjo5CCCGWLFkiQkNDhVwuV9v2j3Xr1k2j52fUu3dvrWNzQ5cuXURSUpLy9rt374SNjY3k+J49ewohhFi/fr04ePCgEEIIFxcXjc4vhBALFiwQFy5cEO/fv9fotd+1a1chhBAzZ84Uf/75pxBCs/eO4rmK14EQQjg4OEiOf/nypejatatwcnISAwcOFA8fPpQcmx1Nzu/g4CDu3LmjvH3//n2N/v5CCPH69WsRFRUlnj59KiIjI0VoaKjk2FevXmW679ixY5JiFa8dXXTo0EHI5XIxa9YscevWLfH8+XONjtu3b1+Rlpam1bmdnZ2FXC7PdJ8mwsPDM9135coVSbHnzp3L8j+pfv75ZxEQECDOnj0rRo4cKa5cuSI6dOggOV7h7NmzwtPTU7Ru3Vp4eXmJ0NBQ4e/vLwYOHJhjXFafMfb29mrPl/G9+jHF9UQKxXVecTy5XC7s7Owkx2d17ZDSfoXu3burXHtTUlJ0+ix5/fq16N+/v+TnBwUFiYULF4p3796JgIAArc/7SfekZXT27Fk0a9YMwIfhJ0NDQ8mx/fv3x6pVqxAREYGpU6dq3G3777//4vTp0zh37hwePHiAqlWromXLlpLPL4RA6dKl8fvvv2POnDlwc3PD+/fvc4zRtRdRQZNhyawULVoUb968QZUqVXDt2jU0b94c79690+gYrVq1wsaNG9GqVSuVb7RSevPq1q2LUaNGoWXLliqxUocqAeDEiRNYvnw5Xr9+rdKDKaXrWgih0gNZtGhRGBlJf9uVLFkSgYGBqFu3LjZt2gQzMzPl0K0UxYsXR0pKCipXroybN2/C0tJS7WsnI7lcjrCwMBw5cgSbNm3C7du3kZ6eLjm+WLFieP78ubIn5+LFi5LeNxmHqYYNGwZvb284OTkhOjoa0dHRkoarANUhCyEE7t27p9G8mokTJ2LgwIEwNzcH8GEYw9fXV3L8woULsXnzZqSlpaFMmTJ48eKFRsPVAwYMwO+//w5TU1PExMRg1qxZuH//vrJ3Myf/+c9/MHfuXPz4448qr32pfzsAKFu2LGQyGapUqYLw8HA4OTkhJSVFcnzlypXRp08fNG3aVOXffeTIkdnGKP7Nvv32WwwdOhROTk4wMjLCvn37lEPP6ly6dAlyuRxeXl7w8fFRvm/T0tIwffp0HDx4UO0xmjRpglu3bimnCiiGeqX2jvr4+OCvv/6Ck5MTjh8/jmnTpmHMmDGSYhWsrKzw7bffomvXrpg2bZryWtK0aVO1PUL/+c9/8Ouvv8Le3h7Ah+FjKb2gaWlpGrUxOzKZDCkpKcr3/uvXrzXq0TU0NER8fLwy5uHDhzAwkL7WsWLFiujZsyfs7OxgZGSEw4cPo2TJkli2bBmAnF+DWSlRogSePn0q6bn+/v54/vw5bt68iUGDBmH37t24c+cOJk2apNE5gU98uFPBx8cHEyZMQExMDIQQqFChgkYXWkW37c2bNzXqtp02bRrOnDmDsmXLomXLlhg4cCAaNmyoUYIIQHlhMjQ0xNSpU7Fjxw7MnDkzxxjFoobw8HCVIc+BAwfCxcVF8rmzmhfg6emJL774QlJ8//794eHhgd9++w3dunVDcHAw6tWrJ/n8ALB3714AUBnmkzq+n5CQABMTk0xDXJokaT4+PpgyZQqqV6+u8bBRs2bN4O7uDmdnZwAfPoCaNm2q0bl1udA7ODhg6NCh8Pf3R8+ePfH3338rEw4pxo8fD19fXwwYMAAVK1ZEjx49NLrQTJo0CUOGDEFkZCQcHR0RFxeHxYsXq437eJiqSpUqOHLkCI4cOSJ5uApApuHCMmXKYNGiRZLb36JFCxw/fhx3796FkZERqlatKvnLGQDs27dPp+HqYcOGYeDAgXBycsK6devQu3dvLFiwQFLs9evXAXyY06mgyd8OAGrUqIFZs2ahd+/eGDduHKKjo5Gamio53tzcXKPXG/C/fzMTExOYmJgo5w9KnfQOAKGhoTh//jyio6OxZMkS5f1GRkbo2bOnpGNMnDgRV65cQVxcHKpWrYo7d+6gUaNG6Natm6T4xYsXK6+92nw4Ax+u125ubpnuNzAwyHFOMgB4e3tj48aN2L59O4yMjGBpaYk+ffqoPWedOnWwc+dOdO/eXeX+3bt3o2LFipLb3q9fPwwYMAAxMTHw8fHBkSNHMGLECMnxo0aNgpubG549e4bhw4fj6tWrmDNnjuT4KlWqoEqVKkhJSUFKSopGHSPAhznliuu9EAJPnjyRNNQOAKdPn0ZAQACcnZ1RsmRJ/PHHH3BwcNDudaB1H1whsmXLFiHEh6GD169faxyvbbdt8+bNxU8//SS2bNkiIiMjNT5vbnB2dhZnzpxR3j5x4oRGXb7u7u5i27Ztwt7eXrx//14sXLhQDBo0SHL8mzdvlEMWiYmJ4ubNmwXyt3jz5o3WsZoOsWQkl8vF5s2bhbu7uxg5cqTYtGmTSE1N1fp42oiPjxdCCPHs2TNx6NAh8e7du3w9f0pKirh79664ffu2eP/+vcbxL1++FEJ8GCrWdLhz4cKFGp8vowcPHohZs2YJT09PMWnSJDFhwgTRp08fyfG6DlcLIcTp06eFhYWFyvtYCsV1TxdpaWniwoULQgghjh49KmbNmpXlEGJ+yTh8JYUuw0xWVlYiJSVFTJ06Vdy7d0/cvXtX9O3bV3K8i4uLSEhI0Pr8QgiNhgezos1Qe3R0tOjUqZNwc3MT/v7+ws/PT/Tr10906NBBPH78WKPz37t3T2zatEn8+eef4vbt2xq3PzY2Vhw/flwcOXJExMTEaByfmJgobt++LdLT00ViYqJGsRmHuM+fPy/u3bsnOVYxVK/IHRITE7X+t/wsetI2b96M3r17o0yZMlrFa9ttGxoaips3b+L06dOYPHkyXr16hSZNmqBly5Zo1qyZckKjOgEBAZg3b55WKxRnz56NiRMnat2L+OTJE/Ts2RNbt26FsbExPDw84ODgoDYuu5WxpUqVkrQyNiNdVijeuXMHY8aMQXJyMrZv346+ffti8eLFyondOVEMuVWrVg2zZ89G+/btVYYqpQwbzZkzBw4ODpK+wWbk7OyMgICALFcnymQyld6RrCi69LMSHh4uuas/q/ObmZnh5MmTkuI/Xt0ok8lQrFgxVKtWDd27d1fbK7Vx40bs2bMHAQEBePXqFYYOHYr+/ftL7g05fvw4xowZo/XEeV0XXmg7XJ1xZbr4/5WZI0eOVK5sk9KLrLjuaePjVZEXLlxAqVKlYG1tjbi4OLXxWb1uAM2uXQcPHsTy5ctV3vfJycmSao0pVteeO3cuy8UXUhZUmZmZoUiRIqhWrRrCw8NhZ2enXOUqha4LDwDg66+/Rr9+/dCgQQOVY0h5/2ZcGa7JUHu5cuUQGBiIffv24c6dO5DJZHB2doatra1KG7Lz8apIxerYO3fu4M6dO5JHMeLi4nDgwAHlNBPFa0bqtevMmTOYNm0a0tPTsW3bNjg4OMDf3x+tWrWSFH/o0CGMHDlSZWXy+PHjJS18sLGxwZgxYxAXF4cNGzYgKCgIXbp0kXTej30WSZouL3RAt27bunXrom7duhgyZAgSExOxf/9+LF68GA8fPkRYWJikYyxbtkzrFYrff/89goODlYmlpnWGtJ0XoOvK2Ix0+aCcNWsWli9fjrFjx8Lc3BzTp0+Ht7c3du3aJel3UHj+/DnCw8OVt6UOG1WuXBlz5sxBXFwcunTpAgcHB0lL2BXzJzOuTgQ+1Cvy8PBQG69w/fp1PH/+HDY2Nsp5GZrU99N1daShoSHi4uKUF+aQkBAkJibCwMAA3t7eaj8st2/fjh07dgD4MIS/Z88e9OjRQ3KSVrp0adjY2KBu3boq732pq57lcjlGjRqFtLQ0fP/99+jVqxd69eolKRbQfrh648aNks+RHV2ue4rX/ps3b/D48WNYWFjAwMAAV65cQc2aNbFt27Yc4z9+3WrDz88Ps2fPxh9//IGhQ4fi9OnTeP36taRYXVfXAh+GalevXo3mzZsrP5g1mU87fvx4rc+toMlK2o/psjK8ePHimYY7pcpuRbKC1CRtxIgRMDU11Xp18sKFC7FlyxYMGjQIZmZm2LRpE3799VfJSVpgYCD++ecfLFq0CLVr1wYA3L9/X1Ls4MGD8ffff6N8+fJ49uwZ3N3dJc0jzcpnkaTp8kIHPryo6tWrh3PnziE9PR0rV65U/qOp8+DBA1y+fBmXL1/GlStXUKJECfz444+YMGGC5PObm5trlaABquPqGUn9Nufu7q7VvADFh+CaNWswePBgzRr9EV0+KJOSklQKGLZs2RLz58+XFJvdB2VCQoLkXlBXV1e4uroiKioK+/fvx4gRI1CiRAls3bo1x7grV65g0aJFKgnZ9u3b4efnh/79+6s9r+KDuFevXti+fbuyKOVPP/2Efv36SWr7x4oUKQJbW1tlSQopbt26hT179ihvt2vXDt27d8eSJUsk9cimpqaq9LZpWkxTMRdQW7ouvDA3N1dWH9dkPooikc6uVpOURFuX657itT9o0CAsW7YMlSpVAvChDMa0adMkHycpKQnLli3DmTNnkJ6ejmbNmmH06NGS5pd98cUXaNasGS5fvoz4+Hi4u7tLnk+rqK3n7OycZTFdKXx8fHDy5EnUr18fnTp1wr59+zB9+nTJ8dr23mak6eT2jLQtAaJrL6iuZZ8U4uLisGnTJq3j5XI5ypUrp7wttbajwrfffosZM2bA3d0do0eP1qgnbPjw4XBwcICHh4dGc1iz8lkkaSNHjsSrV69w7do1pKeno2HDhvjqq6/Uxunabdu0aVOULVsWTZs2hZWVlUaF+DLSZYWiu7u78ue0tDQcPXpU8qR/AGjdujXq1auH69evIz09HTNnzpT0t1Po27cv/Pz8tLpIK+jyQVm6dGlllz0ABAUFafxvcPz4cVy8eBHDhw9Ht27d8OrVK4waNUpyva74+HiEhobin3/+QXp6uqRvcuvWrcOgQYPw22+/4aeffsKUKVNw7949/P7775KLWQKZh+ZTU1Px5s0byfG6ro5MSkpCTEyM8mIZGxur/LeTskq0Q4cO+Omnn2BrawvgwxBExuK26jg7O2e5Y4NU2i68+PiDzsjICAYGBkhJSUHJkiWzLbL6MV1qNenyAa8QFRWlTNCADyuqo6KiJMfPnDkTxYsXV36x27FjB7y9vSUlC8WKFUNERASqVauG8+fPo1mzZoiPj9eo/atXr8bq1atRunRpyGQyjYqKvn37FhYWFoiKikL79u3Rvn17jc6dsSc+LS0N4eHhsLS01Gh1rS7TDbQdandzc8PFixfRsGFDdO7cGZaWlholnEOGDMHq1auzLSYvtaBrzZo1ERYWpvFCM4Wvv/4ax48fh0wmw9u3b7F582aN6pPKZDI0aNAAmzZtwogRI3Dz5k3JK/N79OiBffv2Ye7cuWjVqhUcHBw0WjCm0g6h6deLQujvv//G5MmT0bBhQ8jlcly5cgU+Pj5qux+zqxauoO4bw4sXLzRe2aRJO7T9xtK9e3e18xJymtMESP8A8PT0RPHixZVFeHfs2IH4+HiNijpu2rQJx44dU35QVqpUCXK5HL///rva2MjISEycOBE3btxAsWLFUKlSJfj5+aFq1aqSz9+1a1f4+vri8uXLuHjxIqZNmwY3NzeVHqLsDB06FLdu3UKnTp1gb2+PBg0aSD5vYmIiBg8ejPv376Nz586YOHGiSjkPKdatW4eAgAC0bt0acrkcJ06cQL9+/SQnmB+/9sqUKYPevXtLXuUVEhKCuXPnwsLCQlnOY8qUKbhz5w7evn2LKVOmqD3GgQMHcOHCBRgZGaFx48Ya7digawkM4H89p8+fP8eNGzfQqlUrydtCeXt7o1GjRnBwcIBMJsPBgwfx999/Y/bs2ZLPn9GbN2/g4eEhadhK1/mEwIchOwMDA9ja2kIul2Pfvn0wMTHBrFmzJMU7ODggKChI5b7OnTsjJCREbez58+exefNm+Pn5oXfv3oiMjES3bt0wceJEye3v0KEDduzYoVUxXUWSIYRAWloaXr58iTp16mD37t0aHwv4UMl/7ty5kovBfizjdAN1n03Ah8+fv/76CwMHDsS8efMQGhqKIUOGwM7OTm2sEAKXLl1CSEgIrl69CktLS9jZ2Um6fkVHR8PMzCzbchXqeoEVf/fk5GS8evUK5ubmMDQ01Lhqf2xsLHx8fBAaGgohBJo2bQovLy9lcWR1nJyclF9SU1JS4OnpiZCQEEnzKRWSk5Nx4sQJrFmzBq9fv8bx48clxyp8Fkmai4sLlixZovxgefz4MUaOHKks7SBFamoqIiIikJ6ejho1akjKqNUNK2kygRTIeg9RdTJ+61X0hPj4+ODw4cM5xuVWkqbLRTqjjB+UYWFhaNGihUa9ce/evYNcLpc8TJlR165dsXv3bowYMQIODg6wtraGvb09goOD1cYeO3YMrVu31qg2WkZJSUkYMmQIWrRoIXmXgo+FhYXh/PnzkMlkaN68ueSheiDrLxrXr1/XqDfv1atXuHTpEgwMDGBhYQFTU1O8efMmx/mRubGtD/Dhgh8UFJRpXs7q1atzjNu+fTt69uyZ7ftA6utfsQAkI0dHR42uPRmlpKSgS5cuOHTokEZxmn7AZzzfpk2bcP78eQAfSpK4urpKLiNkb2+PzZs3K3vv3759C1dXV0nvnY9pc/1zc3PDhg0bNC57lJXr169j8+bNkqdLZMXGxkajRVNZ0eX1ow25XI5z585hyZIliI6O1qh25rFjx3D+/HkYGRmhRYsWaNGihdoYdbXINN0zW921JjuKfU8zunLlCiwsLCTF379/H3/99RcOHDiAb775Bg4ODhqVflL4LIY709LSVL75V6xYEXK5XHJ8WFgYRo0ahdKlS0Mul+Ply5dYvny52m8VcXFxiImJgY2NDdq2batxL4iCLisU+/btq/zZwMAAZcqUUc6RyUnGDyHFZu6PHj1CRESE5FoxwIfE8O3btyoXaU0vmJGRkbh69Srs7e2xfPly3Lp1C6VLl4alpaXa2I/n5ClWF1atWhVDhw6VdNH/6quvMGvWLISFhcHPzw/z5s1T222e8YMwq4RYXS9oxnYnJSVh8eLFOHHihHJ+g9QEX9EDoOhJ0HSFlaIumq2tLVJTU7F48WLs379f7YU6u+RG6gqtbdu2YdasWTpt6wNoPy8nt767Fi9eHLt371b2RO3du1ejDwxdajVlpM18QuDDCtG+ffuqXDP8/PwkT4rv378/unXrphyiPnbsmOQ5qrmx7682xXSzU79+fUyePFny8z9Ohh88eKDx3GJtphsoelAVPU8f/1+TnqCwsDAcPHgQR44cQfny5TF8+HDJsQsWLMClS5dga2sLIQSWLFmCGzduYMiQITnGKZKwu3fvYuXKlVi0aBEePHiAadOmSe7BBT5cazw8PLT63AQ+FJLWdj63vb09DA0N4eDggD///FNy711WPoskrXz58tiwYYOyCOGuXbs0ysZnz56NRYsWKZOyq1evYtasWWpXCO7duxcREREICQnBb7/9hu+++w62trZo3bq1RpMJdVmhqLigpaam4tChQ9i2bRt8fX0lL81fvnw5Hj16hDFjxsDV1RU1atTA0aNHJb9ZdLlIK3h6eqJv3744evQoHj58CE9PT/j6+ipX/eWkevXqMDIyUlbn3rdvH54/fw5zc3NMmTJFbY8h8OFic+TIEfTr1w8lSpRAxYoVVeb6ZUWXVWUA1B5fqrFjxyIqKgrVqlVTueBITdL++9//YvLkyTh48CD+/fdfNGnSJFPPaE60XV2qeH3puspR23k5ioUpFSpUyLT4QOq+o8CHhGbWrFmYPXs2ZDIZWrZsqVEJnIyvA5lMhjJlykieAK3rfELgQ0HWkJAQLFu2TNmjGhoaKjm+a9eu+OGHH3DhwgXI5XIsW7ZMcqKSG/v+alNMV+Hja8P9+/dRtmxZyfEZrwEymQw2NjZo3ry5Rm3Qphizritrr127hgMHDuDYsWP49ttvYWtri61bt2rcG3XixAns2bNH+Zrr2bMnunbtqjZJU/Dy8lJWUahWrRqGDx+OKVOmqF10pTB79mytPzcB3eZz+/v7o1atWkhISNCoQygrn3SSphiq8fHxwaxZs7Bq1SoIIdCsWTO1FfszevfunUqvWcOGDSVPXK9SpQpGjBiBESNG4N69e9i/fz9Wr16NatWqYd68eZKOocsKxcePH2P79u0ICAhAXFwchg4dqlKBW52jR49i27Zt2LBhAxwcHDBhwgSNdiz4+CL922+/Sd7aReH9+/ewtbXFlClTYG9vD0tLS8lbl1y7dk1l7ljt2rXRtWtX+Pv7Z7tyTuH48eOwsrLCkSNHAHzo6r5y5QpMTExw+PDhHBOdjB/sd+/exfnz55GWloamTZuiTp06atuta5KnEB4ejv3792u90uybb75BkyZNsGvXLhgaGkqu75dbq0t1XZ2sbQmMDRs2ICEhAdu2bVMZfklPT0dwcLDkOX0VKlTQuPcqI11qNem62wLw4fo1ePBguLq6Yt68eZJ6rzMKDw/HqlWrMvWGSJkTWqxYMXTt2hVPnz7FF198gdmzZ2t07QFyZ/GEQuPGjSXN51Lo2LEj9u7dC1dXV7x48QLbtm3T+H09d+5c3Lp1C99//z3i4+MRFhamdj6orlNVevbsiW+++Qbt2rVDmTJl8Pz5c5VVllL/pl9++SUSExOVr93U1FSNppskJSWhTZs2ytstW7bUaC6zLp+bQOZrcIsWLdC9e3eMHj1abWzx4sXRrVs3PH78GHK5HBUqVMCiRYtQpUoVyedX+KSTtKFDhyIgIABly5ZF/fr1JW1Hk5Uvv/wSR44cUU5YPnLkiMbfKtLT0/H8+XO8ePEi0x6Q6mizQvHw4cPYtm0bbt68iY4dO8LX1xdTp07V+KIll8thbGysLAoql8uRlJQkOb5Lly5wcnKCo6OjynJoTRgaGuLgwYM4ceIERo8ejSNHjkjewy01NRX37t1T1lW7d++esiimuu1tbty4ASsrq2zr/kjpjQoMDMSyZcvQoUMHyOVyjBw5EsOGDZO8tYyuqlWrhpiYGK272+3t7dGoUSOEhIQgJiYGkydPVv5OUui6ulTX1cnm5uZwc3NDeHg4unfvjvHjx0sabq9UqRJu3ryZ6X5jY2PJX66AD4uWFi9ejLi4OI33fQV0q9XUpUuXTFvhHDp0SKOtfWQyGTp27IiKFSti9OjR6Nevn0a9cRmvOZr2huiy7292xaA1GfLTNcEbN26c8gupiYkJ5HI5JkyYgN9++03yMRYsWICbN2/i999/R1JSElasWIGLFy/mWk97VkaMGKFT+RDFMK9cLoejoyPatWsHQ0NDnDp1SqMFW6ampti6dauyVM9ff/2lUU+mriv7P57Pff/+fcnXLm9vb/zyyy+wsbEB8GEB1bRp07QbGdBqn4JCQrGNkxD/29pJGxEREaJbt26iSZMmokmTJqJr167i33//VRuXkpIijh8/LiZOnCisrKzE+PHjxdGjRzXeGufRo0eiV69eom7duuI///mPcHFxUXv+WrVqidGjR6tso9OuXTuNziuEEPPmzRN2dnaia9euIj09XfTu3Vv4+vpKjn/y5IlYtmyZsLGxEYMGDRL79+8XKSkpGrXhzp07YtKkSeLAgQNCCCHGjBkjeYuRs2fPijZt2ggXFxfh5OQk2rVrJy5evCh8fX3F6tWrNWpHfHy8SE5O1ijGwcFBvHr1Snk7NjZW561eNDFw4EBhYWEhevbsKdzc3JT/SXX06FGV2+np6WLt2rWS49euXSs6d+4s5s2bJ+bMmSM6deokNm/eLDk+K5psa3bu3DnRpk0b4ezsLJycnETHjh3F9evXJcffv39fmyYqderUSRw7dkw8fvxYPHnyRPmfVI6OjuLq1auiQ4cOIjg4WAih/lr2119/iYCAAGFlZSUCAgKU/+3cuVN06NBBo/ZnvIbGxsaKvn37inr16kmO79KlS47HzElISIjo37+/iI+PF9bW1qJz587i119/lRT7xx9/CCGEVlsRKbRu3VrUrl1bed1X/Ozi4iJu3bqlNt7e3j7TfQ4ODhq1wc7OTqSlpSlvp6amZvk3zW2bN28Whw4dEkII0bVrV2FlZSU6duwoaVu2PXv25PifVE+fPhWDBw8WDRs2FE2aNBHDhw8Xz549kxyf1efmgwcPJMdbWVkp/2vXrp3o2rWrOHHihKTYrF7j2v67fdI9aR9/g9LWmTNnsHPnTo1XCDZr1gxffPEFOnXqhBkzZigXDly7dg2A9BVq3333HbZu3arR+YOCghAQEIA+ffqgQoUKsLOzk1SX6mOKDX7Nzc1hYGCAqVOnShquU6hQoYJyuPfw4cOYPXs2vL294eDggOHDh0vaqqtWrVrw8PCAmZkZLl68CEtLS3z33XeSzt+0aVMcOXIEd+/ehYGBAapVq4YiRYqgUaNGkr8t3r17FxMnTlR+s6patSp8fX0l9UjI5XKV39HU1DRXilxKJXX+R3batWuH4OBg3L9/H0OHDsXBgwfxyy+/SI7/5Zdf0KxZM5w/fx4GBgZYsmSJRqtLdfk2CwDz5s3D6tWrlT0aN27cwIwZMyTPS4mKisKECRO07gkrU6aM1pXGAe1qNSUkJODKlStITExU6QU2NDTUaLcK4MOcVAVTU1P88ccfGq1O/Lg3JCQkRFJvyPHjx1GvXj3Y2Njg6NGj+Prrr1G0aFHJvZibN2+GlZUVxo0bp7ItnYKUelmNGzeGjY2NcgTl5MmTOHDgANzc3DBjxgy1uy7IZDKEh4crX3sPHjzQeJV3WloakpOTlTU6pWxur2sv4po1a5RbKgEfppts3LgRx48fx+rVq9UWM9d1qodC+fLllauw4+Pj8fz5c3z99ddq4zIu2KhcuTLKlSsHExMTVKlSRaPRHE0WqHzM2NhYuUId+LAAQ2rZno990iU4Mi5/z2opvFRdunTBvn37NI5zc3NTuZ1xlZbUFWqnTp2CqakpatWqhdmzZ+Py5cuoV68exo0bJ+lil56ejuPHjyMgIAAnT55ULqHPONafE2tra1SqVAnOzs7o0KGDxhOPExMTcfDgQezduxcvXryAk5MT7OzscOrUKezevVtSrTFvb28YGBjA1dUVP//8M1q2bIn4+HhJwwa67Pup0KtXLwwbNkz5Nzt8+DD+/PPPHKthK5Z9jxs3DmXKlFFZtPLmzRuN5lbo6uTJkzh79qzyQqlJnTF/f388f/4cN2/exM6dOzFs2DDUrVtXcvV8IQS2bt2qLGbctGlTuLm5SR6uViw4kclkyonzI0eOlPz6dXFxyfQa0+RaYG1tjUmTJmXamkbqwiM/Pz+kpaXhxx9/VClELfULmi61ms6cOaPxRPWP6fr+iYqKwowZM3D+/HkUKVIEjRs3xtSpU3P8sF2/fj1CQkIwf/58pKWloVevXpgyZQru378PuVwuqbbe0qVLERQUhOfPn2ca6pdaayurUheK15OU11BoaCjGjx+vXLjw+vVr+Pn5aTSvb8OGDdi6davyfXDq1Cm4urpqvBewJmxtbbFr1y5lYqh4Dcrlctjb2+Ovv/6SdJyPp3ocPXpUo6keO3fuxOXLlzF+/Hg4OTnBxMQEnTp1UvtFI6t/FyEEwsPDcf/+faxfv17S+a9fv45Lly7B1dVVWe9yxowZsLa2Vht79epV/PrrryhdujSEEIiLi8PChQu12gXkk07S6tWrp3yDZKz3JDQsivfLL78gJSVFqz3wHjx4gFKlSsHMzAxr1qzB5cuXUbduXQwaNEhtSQ4fHx9cvXoVqampKFu2LMqUKQNbW1ucPXsWjx8/1nhC8qtXr7B3714EBARotELv4sWLCAgIwNmzZ9GmTRs4Ozvjhx9+kBTbrFkzWFlZwcXFReWDSfz/htEZv6lnx8XFBbt371bOg1JsDyMlwXN0dET79u1x/Phx5b6f3377rUbbu2R1Qc744ZmVpk2bolmzZnBxccH58+dx7tw5yOVyNG3aFCNGjNCqXps21q5di0OHDsHe3h5CCAQHB6NDhw6Sa645OTkhICAAzs7OCAwMRFpaGhwcHCTXuZs/fz4ePXqErl27QgiBPXv2oEKFCpI+aHV57yjqq23btg0mJibo1q0bjIyMEBwcjMTERMkLh3r16qW2xyQnH39RAzQrIaJLrabcKGGRG+8fTTk4OCgXm/j7+yMqKgoLFy6EEAKdO3fG/v37JR/L29sbM2bM0Kodbm5usLOzg4ODA+RyOYKDg3Ho0CF4eXnh119/lVSrLCUlBXfu3MGpU6fw999/4+7du7hy5YrkNrx69QpPnjzBxYsXYWRkBEtLS7UrXHVdOPBxp8TBgweViYkmX3AcHR2xYcMG5UjCq1ev0K9fP8kdHi4uLvj9998RFBSEiIgITJkyBT169JB03c+OnZ2d5CSzR48eGDduHF68eIGQkBBMnToV7u7ukosZp6am4uHDh5DL5ahSpYpytxFNfdLDnQcPHsyV42i7B97q1auxdetWGBoaokmTJnjy5Ak6duyI8+fPY9q0aWqX4oeGhiIoKAhJSUlo27Ytzp49CyMjI7Rv3165TY4mTE1NMWDAAAwYMECjOEtLS/zwww/Yv38/Fi1ahGPHjsHU1BTTpk1T+7c5evQojI2NERERgdu3bysLActkMkkJGvChN1DxTWzGjBlISkpCcnKypFhd9v1UDLXVrl0ba9asQbdu3WBoaIjg4GC134ZPnDiBQ4cO4b///S8iIiLg6OgIFxcXjSZt54agoCDs3LlTmdT06NEDLi4ukpM0RY+XohcpJSVFci8YAPzzzz8IDAxUxrRt2xb29vZq41atWoVt27Zp/d75uL5axp5LTYab//Of/2Du3Lla94TpWkJEl1pNuVHCQtcN5rVZOCGTyZRDQ+fOnVP2GmkzTcDLywsnTpzINEQuZdGPv78/fHx84OfnB0NDQ+XqwIMHD2Ls2LFq4xUr6/fs2YO3b99i6NChWLlypUbtd3V1xf79+zUqHq2gbfkbuVyusj+xIkHTdEuu3JjqUbp0aZw8eRL9+vWDkZGRRvvmZhQTE4MjR44oewelkMvlaNKkCcaOHQtra2uUL19e7ZShV69e4Y8//sCXX36J/v37o0aNGpDL5di6dSuWL1+uUfkahU86SdO0MnF2SpYsiS5dumi0ZyXw4QNy//79ePfuHTp06IDQ0FAUL14crq6u6Ny5s9p4IyMjGBoaomTJkqhQoYLKN+qMHxh5KTQ0FHv37kVoaCjatGmDRYsWoVGjRggPD8egQYNw6tSpHOMjIiK0KgSckZOTE1q1aoVGjRqhQYMGsLW1lfxBocu+n3379lUWgTx37pxKj4pMJoOXl1eO53V0dISjoyOio6MRHBysLKXQrVs3SYlKbhBCqPQ6FS1aVKN5MTY2NhgzZgzi4uKwYcMGBAUFabTRcHp6OtLS0pR1AdPT0yWtrgwODtbpvaNrcqRw/fp1AB82ilfQpCdM1xIiuqxuzY0SFrpuMD979uwsh4tzYmhoiLdv3+Ldu3e4ffu2coXq06dPNZ7TNW7cOK3rBJqbm2dK9pOTk7PsHc3o45X1fn5+Wq2sBz58QQwMDET9+vVV3sc5zanTtfyNvb09Jk6ciPnz5ysTtcTEREyePFk5t1CKWrVqwcfHR2WqhybzUatXr44hQ4bgyZMnaN68OUaPHi15BOdjjx49wvXr1zWqUVi8eHH8/vvvOHfuHKZNm4Y///xTbZI3btw4mJiY4PXr10hNTUWbNm0wYcIEJCUlabTTR0afdJKWW168eIEePXqgSpUqcHBwQKdOnSRNAjQyMkLx4sVRvHhxVKxYURljaGgoKT5jj8XHvRf5Nfl8+fLl6NatG6ZPn67S5lq1aknauUDbQsAZDRgwAP369VN+uG/evFnyXnzabpAN6DZxNCMzMzP8/PPPsLOzw4oVK+Dp6ZlvSVqzZs3g7u6unMwbEBCg0Ua/gwcPxt9//43y5cvj2bNncHd312jvR3t7e/Tr109ZX+qvv/6SlOTp+t5R0DVJUiR7iqKUmpT/AHQvIaJLrSZdSlgoODo6Znr/SJm8raDNwonBgwfDyckJaWlp6NatG8zMzBASEoJFixYpi5tKFR4ervU2TAcPHsTy5ctVhouTkpJw9uzZHOPc3d1hY2OD7du3Kzen1/Z6fe3aNeVCMwWpU3W0LX8zePBgTJ8+HT/++KMyub1//z4cHR01GoWZPXs2li5dismTJyv3zvT29pYc7+bmhuTkZNSoUQPGxsZwdHTUarcN4MNokKY1/vz9/bFz504sXboUX375JaKjo7FgwYIcYyIjI3HkyBEkJCSgV69e2LJlC9zc3NC/f3+NCthn9EnPScttFy9eREhICP755x/Ur19f7eTvnBYuSBnbz2lOXUxMDG7cuKHLryPJgAEDJG3mnJ2s9u6Uuu/l1KlTMWvWLJ0+aNPT05GUlKSyQXbLli012vdTlw3u3759iwMHDiA4OBgvX76Es7MzHB0dta6Cro0tW7bg3Llzygtlr169dNrLsFGjRrh8+bKk5969exe3bt3Cxo0bUb58eXTr1k3SpH9d3zsKij0nAdUkSUqSA3wYsvLw8MDjx48hhED58uWxePFiVK5cWVJ8Vrp37y55g/esVrfOnj1b7d67ALB//37s2LEDv/32m3Kovnbt2mo/aADV3Qri4+NRqlQpREVFoUSJEihSpIjkYr7aLpxQ1JNU9LycPHkSxYoV0+gLBvCh5pe3t7dWdQI7dOiA2bNn448//sDQoUNx+vRpvH79WrnqMTt3795VzvtVrKz/448/cOLECY3boIt169YhICAArVu3hlwux4kTJ9CvXz/J/3YvXrxQ9iTXrVtX0orY3GRra6vR/EN9kHGucqtWrfDbb79J3uszO+xJk0gIgdTUVKSmpkImk0nKih8+fKjsXs74sxACjx49UhufW3PqdJGSkoJnz57hm2++0ShOMdlcl0LAvXr1QlRUFFxcXFRWxgLSv5m2b98eFhYWaNu2Ldq0aYOOHTtq9HsAqr0Zig96dUUZQ0JCEBQUhCtXrqB9+/YYPXq0xt/kdJHV8nvgQzHT2bNna7R/38ekfK+LjY3FqFGjcP/+fVSqVAmGhoY4e/YskpOTYWFhobY3Sdf3joIuPVEAMG3atExFKadOnSp5OFXXEiKKIXfgf9tC5TTMrqBrCYtJkyahbNmyaN68ucYrujPSdrj44+2cpK7m/VhycjJsbGxQs2ZNlWu2lC94X3zxBZo1a4bLly8jPj5euWBJnZo1a2LixIkYN26ccmX9y5cvlTs3aPK7xMXFwc/PD5GRkViyZAl8fX3h6ekpqTc2Y/kbmUymcfkbc3Nzra6XChs2bMCKFSuUc9mklgBRqF69OpYtW4YGDRqoDPVKnQ+qrY+vnQpS2p8x7quvvtI5QQPYkybJrFmzcOTIEdSpUwcODg5o3769pDlhGb/FZ0WbrX8UvUv5xcbGBo8ePULZsmVRtGhRyStjFb0dERERmDBhAiIjIyGEwHfffQdfX19JlafbtWunnBOmIJPJEB0djbS0NElv9rS0NFy6dAmnTp1Szmtq27atxvuHZiSEQO/evXNc9efq6goXFxfY2tpq1GuXF9StRNWUlJ60MWPG4LvvvoO7u7vyQz41NRVLly5FTEyM2mQht947uvREAVn/7aT2BAP/KyECfHjtmpqaSi4hcvz4cVSrVg3fffcdDh8+jF27duH777/HiBEjcpyblRslLG7fvq0cNahduzY6d+6MFi1aaLRoRB9k9zqS8vrp06cPfHx8cPfuXdy4cQOjRo2CnZ2d5NdORtqurB81ahRatmyJzZs3Y9euXVi+fDlu376NNWvWqI1NSUnByZMnkZiYCODDqMKTJ08kf0HRVbt27bBp0yate+B0XRldEDp16oQ5c+ZALpdj6tSp8PHxUfn80ibBZE+aBJUrV0ZAQIDkeVAKubX/YkZhYWG5fsycSK0pk50qVapoVQgYyDwnLDExEfPnz8fp06clJ6pGRkaoUaMGXr9+jeTkZBw9ehQHDx7UKUl78OABoqOjc3yOJnXY8po282GyG2IWQkiaOB4eHp5pG7YiRYrg119/haOjo9r43HrvaNsTpaBrUcpt27ZpNdSWMdG6c+cOxo8fr0y05s+fn2OitXfvXpUSFu3atUP37t2VJSykqFOnDurUqYOxY8fixo0bCAkJwcKFC1GvXj3Y2dmpHXbMaaqCTCbDn3/+KakdutLldeTh4YHFixfDz88Pa9euxfbt27Xezk3blfVPnjxBz549sXXrVhgbG8PDw0Py5P2RI0ciKSkJkZGRsLS0xIULF7SuVKCNatWqabzYLiNd54MWBHNzc+Xe2GZmZir7ZGubYDJJk6Bnz574/fffERERgalTp2LDhg0YPHiw1hMBdZHfHZ9mZmYIDQ3F69evVe5Xt3L23r17aN++fbaPS61Rp3DmzBl4eXmhZcuWCAoKkpzsde7cGW/fvkXnzp2VK4Q0fbNn7P4WQsDU1BS//vqrRscoSNq8ZnTdGzC7nmaZTJZvvTHHjx/Hhg0bMvVEfbyfZU6mTJkCd3d3laKUmmxS7ubmhkqVKsHFxQXt27eXPHSoS6KVmyUsAOCHH37ADz/8gIsXL8Lf3x/BwcFqa3317NkTQObXkRBC67plmtBlyCrjHNQSJUrA29sbtWvXRseOHfO0iGxWDA0NER8fr/xdHj58KPn9ExERgUOHDsHHxwddu3bFhAkT8q0XDfjw2re3t0eDBg1U5sBKmcsL5M180LyWW6vKM2KSJsHMmTNhamqKmzdvwtDQEJGRkZgyZUq+Vo1XmD17dr6eb/To0YiJidF4CXulSpUkdcmr8+7dO8ybN0/Ze6bJBywA9O/fH2fOnMH58+cRGxuL2NhYNG3aVKM3+p07dzRstX7R5sNZ156snM6ZHyuTdemJAj5Mmp41axYePXqE5s2bw9nZGaVKlUKVKlU0+nJ28OBBXLhwAYGBgfDz85NcDFqXRCu3SlgIIXDhwgUcOHAAp06dQp06deDm5iZptWa9evUAZP06ev78ueQ2aEuX92xWbVZUrB8zZozOowuaGDVqFNzc3PDs2TMMHz4cV69eVbstk0LZsmUhk8lQpUoVhIeHw8nJCSkpKXnc4v/x8fGBvb291qWwdJ0P+qlgkibBzZs3ERAQgFOnTqF48eKYP39+vpVQAFS3p1i4cKFG21Po6t9//9VqCXuRIkV0rlOXsfcsODhYo0KECj169ECPHj0gl8sRFBSEFStWYPr06RpNnI+MjMTVq1dhb28Pb29v3Lx5E56envm6EEBTivl8wIeEQ9GrqeluG9rKridVsTI5r+k65Dd58mTUrVsXPXr0wP79+7F7927JPQAfa9y4MerXr69RMWhdEq3cKGHh7e2Nv//+G99//z1sbW0xbty4Ap9bmV8y7j35MUUpmbyWcR6kvb09ihUrhvT0dDRs2FDywpMaNWpg1qxZ6N27N8aNG4fo6GhJe3/mFmNjY61qwym8fv1amaABH0ZFNC0G/ClgkiaBTCZDSkqK8kPv4/ozeW327NkYP348Dh48iGLFiiEgIAAjR47MlyTtu+++Q1RUlMaTPxs1aqTzuQcMGAAjIyOcPn0a//zzj/J+TRKNbdu24cyZM7hx44aytlvbtm01aoenpyf69u2Lo0ePIiIiAp6envD19cWOHTs0/ZXyTUF/2yzolcm6Dvm9ePFC2WPSvHlzScVPs6JtMWhdEi0bGxtYWFiolLAwMTHB7NmzJZew2L59O0qXLo1bt27h1q1bWLhwocrjeZ3k6xttKtbrIqfVtREREWpfj3FxcfDw8MC///6L6tWrw93dHadPn5ZUfiW3tGjRAvPmzUPr1q1Vfgepk+dzc5PywoxJmgT9+vXDgAEDEBMTAx8fHxw5ckTjooq6kMvlaNy4McaOHYtOnTrhm2++Ubs9ha4UE35fvXoFe3t71K5dW2VegboJkOpqCUmRGx8E9+/fR/fu3eHn56f1HML379/D1tYWU6ZMgb29PSwtLZGWlqZz2/JSbu22UVjPr+uQX8YPlSJFimhdhmLFihXo2rUrZsyYoVJGQF0xaF0TLV1LWOj63tN14Ym+0aZivS4CAgK0Xl1769YtDB48GHPmzFEWf71+/TqOHDminCuYHxRlV27evKm8T5PJ87rOBy0I6nYV0KY3niU4JLp//z7OnTuH9PR0NGnSRKN6M7pSzANRzLMJDAzEoUOH8nQF4fnz5xEXF4e0tDSULVsWwIcLbGxsLL766qs8WbmaFx49eoRr167pNFTZs2dPDBw4EDNnzkRAQACuX7+OVatWabRrAuWvAwcOwNfXF2lpaWjXrh2mT5+u0hOlridClwK6wP8mrit6fRU0rRVVWOVF+aHPlWJ17blz5yStrv3pp58wfPjwTM/5+++/sX79emzYsCGPW5y1jPuB5iTjfND69etrPR+0ICiuEcePH0diYiIcHBxgZGSEkJAQlCpVSnKdwoyYpEmQmpqa5QpHbYdANPXixQvs3LkTLVu2hIWFBfz8/ODm5qbR9iyayurb2KJFi7Bnzx6sXbs2X5NUXbi6uqJv374oUqQI/vzzT4wePVrjocrw8HBs2LABbdu2hbW1NTw8PDB06FDUqlUrD1tOutKlan3G3T4UxzI3N9dqTl9u16mjz5NidW14eHiOq2tz+kLh6OiIvXv35lUTVRw/fhwXL17E8OHD0a1bN7x69QqjRo1Su+PBzz//jLp168LS0lK544C280ELSvfu3bF9+3Zlz6dcLkePHj20+mLP4U4JtF3hmFvMzc3RrFkz3LlzB3Xr1kXbtm3zNEEDgPnz52PBggUqH2geHh6wtLTEvHnzCuzbmKZyY6jyxIkTKheJRYsWYeHChUzS9JwuQ365OacuP+ev0qdD29W1aWlpkMvlmYZG5XJ5vi4cWLZsGXx9fRESEoL69etj2rRpcHNzU5uk5dZ80IIUHx+PN2/eKGurvnz5UuN9cxWYpEmg7QrH3PLnn3/iyJEjiI6Oho2NDaZNm4Zu3brh559/zrNzvn37Nssehx9//BH+/v55dt7cZmhoiIMHD+LEiRMYPXo0jhw5IrnOkL+/P2JjY3Hs2DE8fPhQeX96ejquXbtWqGqlkWZyc04dBytIU7qsrm3cuDGWLVuGUaNGqdy/YsUKZWmU/FKtWjUsXLgQDg4OMDExkZQk5tZ80II0dOhQODg4oFGjRpDL5bh27ZpGRbQzYpImgbYrHHNLQEAAduzYgR49eqBMmTLYtWsXunfvnqdJmr58G9PVzJkzsWHDBkybNg1mZmb466+/4OPjIym2U6dOePDgAc6ePasyh8bQ0BDDhw/PqybTJ4Y9aaQpXVbX/vrrrxg8eDCCg4Pxww8/QAiBW7duwdTUNF9LWHz11VeYNWsWwsLC4Ofnh3nz5mn1GVoY3z9OTk5o0aIFrly5AplMhhkzZijndmuKc9JykHGF47NnzzRe4ZhbXFxcsGfPHuXclrS0NDg7O0veP1AbM2fOROnSpTN9G1u2bBkiIyPzbZWTrq5fv4769esrbyclJWHJkiWYNGmS5GNInfBKpPBxnTrFsGt+1amjwu3p06c5Pq6up1cIgbNnz+L27dswMDBAvXr18r2uY0JCAo4cOQILCwtUqlQJmzdvhpOTk9oyJrk5H7SgvH37FsHBwXjz5o1KT7o2deOYpOVAX1Y4zps3DzKZDMeOHcP48eOxfft2VK5cWdJGydpKSEjA4MGDERMTk+W3sdKlS+fZuXOTtbU15s+fj4YNG+LkyZOYOXMmmjZtKqlqt2ICbnZbzHzqK/RIe7p+yBIVVuoWyaibY/YpvHcGDBiAUqVKoUaNGiqfHUzScpm+rHCUy+XYsWMHQkNDIZfL0axZM/Tq1UujLV60oQ/fxnQVEREBd3d3VKxYEY8fP4a3t7fkYooBAQHKMgpZyakyORHR50hRKywyMhKPHj1CmzZtYGhoiNOnT6N69eq5sl2gvrO3t8+1kS4maTko6HozUVFROT5eUHPkCoOMf7uoqCiMGTMGXl5eyqFPKX+72rVrZ1v1Gyh8y8KJiPKLm5sblixZolzhGBcXhxEjRmDTpk0F3LK8N2HCBAwcODBXOnK4cCAHBb3CsW/fvpDJZHj//j1iY2NRsWJFGBgYIDIyEhUrVizwrXf0Wd++fVVuGxsbK+fRSZ3XoEvVbyKiz1l0dLTKtJjixYvny769+uDevXtwdnZG2bJlUbRoUZ3m0zFJy0FBr3A8duwYgA/1yVxdXZVDjdevX8e6devy/PyFmeJvp4s6deqgTp06GDt2rLLq98KFCyVV/SYi+py1bdsWAwYMQKdOnSCXy3HgwAHY2toWdLPyxbJly3LtWBzuzIG+rHB0cHBAUFCQyn25Oeb9KcqLPdQA6VW/iYg+dwcPHsT58+chk8nQvHlztG/fvqCblC9SUlJw8uRJJCYmAvhQW/PJkycYPXq0xsdiT1oO9KXezNdff40lS5agc+fOkMvlCAoKQuXKlfPt/IVRbq281bbqNxHR5+jChQvKn01NTWFjY6PymNSFW4XZyJEjkZSUhMjISFhaWuLChQto2LChVsdiT5oa+rDCMS4uDkuXLlVuWtyiRQu4u7uzdpdEd+/exfnz55GWloamTZuiTp06kuI+rvptZWUlueo3EdHnyM3NDUDmIrSKeVn5VV+0IHXs2BGHDh2Cj48PunbtClNTU4wePRrbtm3T+FhM0gqJd+/eITIyEjVr1kRycjKTBYkCAwOxbNkydOjQAXK5HEePHsWwYcPQrVs3tbG1a9dG6dKllX/rjy86haGoIhFRfjt//jxWrFiBGzduAADq16+P4cOHfxa9aADQq1cvbNu2DZs3b4aJiQmcnJyURek1xSStEDhz5gymTZuG9PR0bN++Hfb29vD390erVq0Kuml6z9HRERs2bECZMmUAAK9evUK/fv2wb98+tbGfQlFFIqL8dObMGUycOBHDhg2DpaUlUlNTceXKFaxatQr+/v6fxYKrqVOnwtjYGL1798a4cePQuXNnBAcHazWPnHPSCoGFCxdiy5YtGDRoEMqVK4dNmzbh119/ZZImgVwuVyZowIc5ElL3gmMSRkSkmeXLl2P16tUq00q+//57NGjQAHPnzsXmzZsLsHX5Y/r06bhy5QqqV68Od3d3nDlzJtP+q1Kx4FMhIJfLUa5cOeXt6tWrF2BrCoc3b94AAGrVqgUfHx+Eh4cjPDwcPj4++bZTBBHR5yYhISHLeb/16tVDXFxcAbQo/xkaGirnrrdv3x5eXl5aJ6dM0gqBr7/+GsePH4dMJsPbt2+xcuVK7jaghrW1NUaPHg17e3sYGxtjypQp8PT0hJGREby9vQu6eUREn6R3794hLS0t0/1paWlZ3v+5+LiMllSck1YIxMbGwsfHB6GhoRBCoGnTpvDy8oKZmVlBN01vJSUl4dChQwgKCkJERAQcHR3h4uKCihUrFnTTiIg+WTNnzoSxsTEmTZqkvC89PR1z5sxBkSJFVO7/nFhYWGhVW5NJWiFx69YtfP/994iPj0dYWBiaN29e0E0qNKKjoxEcHIygoCCULl0a3bp1g729fUE3i4jok/Pu3TsMHToUz549Q7169ZCeno6wsDBUr14dy5Ytg7GxcUE3sUA0atQIly9f1jiOSVoh4O/vj1u3buH3339HdHQ0xo4diyZNmsDd3b2gm1aoPH/+HCtWrMCePXsQFhZW0M0hIvpknT9/Hjdu3IBMJkP9+vXzvb5oQXBzc8tyYZoQApcvX8bNmzc1PiaTtEKgS5cu2Lt3LwwNDQF8GNt3dnbmtlASvH37FgcOHEBwcDBevnwJZ2dnODo6wtzcvKCbRkREnxBFwfnsaLMTDktwFAJpaWlITk6GiYkJAOTL5u6FXUhICIKCgnDlyhW0b98eo0eP/iy+yRERUcHIre0IM2KSVgj06tULLi4uaNeuHQDg1KlTcHV1LeBW6bfNmzfDxcUFCxcu5O4MRERUKHG4sxB49eoVnjx5gosXL8LIyAiWlpb4/vvvC7pZRERElIeYpBUCtra22L9/f0E3g4iIiPIRhzsLgdq1ayMwMBD169dHsWLFlPezoC0REdGniz1phYBiLlpGMpkMR48eLYDWEBERUX5gkkZERESkh7h3ZyEQFxcHLy8v9OvXD69fv4anpyfevn1b0M0iIiKiPMQkrRCYOnUqfvjhB7x58wYmJiYwMzPDuHHjCrpZRERElIeYpBUCT548Qc+ePWFgYABjY2N4eHjg+fPnBd0sIiIiykNM0goBQ0NDxMfHK/cEe/jwIQwM+E9HRET0KePCgULg77//xoIFC/Ds2TP85z//wdWrVzFnzhy0bdu2oJtGREREeYRJmh4LDAxU/hwbG4tixYohPT0dycnJMDMzg5OTU4G1jYiIiPIWkzQ9Vrt2bZQtWxbNmzdHkSJFMj0+d+7cAmgVERER5QcmaXrs9u3bCAkJwT///IPatWujc+fOaNGiBeejERERfQaYpBUSN27cQEhICM6dO4d69erBzs4OTZs2LehmERERUR5hklbIXLx4Ef7+/ggPD8eVK1cKujlERESUR5ik6TkhBC5cuIADBw7g1KlTqFOnDmxsbGBlZYUSJUoUdPOIiIgojzBJ02Pe3t74+++/8f3338PW1paJGRER0WeESZoeq127NkqXLq1MzBTFbBWOHj1aEM0iIiKifMAkTY89ffo0x8crVKiQTy0hIiKi/MYkjYiIiEgPseAWERERkR5ikkZERESkh5ikEdFn5cmTJ6hVqxZcXV0zPebp6YlatWrh1atX2cZPmjQJ69evz8smEhEBYJJGRJ+hokWL4uHDhyqLc969e4dLly4VYKuIiFQxSSOiz46hoSFsbW0RHBysvO/QoUNo3749gA9FpGfPno3u3bujc+fOsLW1zTKBmzt3Ln766SckJiYiJSUFc+bMgbOzMxwcHDBp0iQkJCTk2+9ERJ8eJmlE9FlycnJCUFCQ8nZgYCCcnZ0BABEREYiOjsb27dsREhICZ2dnrF27VvlcIQRmzJiBp0+fYu3atTAxMcGaNWtgaGiIPXv2ICgoCGZmZvD398/334uIPh1GBd0AIqKCUK9ePRgYGCAsLAxly5ZFYmIiatasCQCoWrUqxowZg23btuHx48c4d+4cTExMlLEbNmxAbGwsAgMDYWxsDAA4ceIE4uPjERoaCgBITU1F2bJl8/8XI6JPBpM0IvpsOTg4ICgoCKampnB0dFTef/LkSaxYsQIDBgxA+/btUbVqVZVet8aNG6NRo0bw9PTE9u3bUaRIEcjlckyePBlt2rQBACQmJuL9+/f5/jsR0aeDw51E9NlydHTEgQMHEBISgi5duijvv3HjBqysrNCnTx/88MMPOHLkCNLT05WP16tXD3379kWpUqWwbNkyAECrVq2wefNmpKSkQC6XY+rUqVi4cGG+/05E9OlgkkZEny1zc3NUq1YNlStXRunSpZX3d+7cGRcuXIC9vT169uyJihUr4smTJ5DL5crnyGQyzJkzB1u2bMHly5cxfPhwVKhQAc7OzujcuTOEEJg0aVIB/FZE9KngtlBEREREeog9aURERER6iEkaERERkR5ikkZERESkh5ikEREREekhJmlEREREeohJGhEREZEeYpJGREREpIf+D13bn7TI6YtZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60dc5d99"
      },
      "source": [
        "We can first look at the correlation between horsepower and price"
      ],
      "id": "60dc5d99"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7954284d",
        "outputId": "88ea6b3c-bb3b-4967-b543-430d677589c9"
      },
      "source": [
        "ax= plt.axes()\n",
        "ax.scatter(df.Horsepower, df.MSRP)"
      ],
      "id": "7954284d",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x273c388af40>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f78689e9",
        "outputId": "1e268ffa-dc6a-4132-c1fa-7cbcc1a37cc4"
      },
      "source": [
        "# Plotting a heat map\n",
        "plt.figure(figsize=(10,5))\n",
        "c= df.corr()\n",
        "sns.heatmap(c,cmap=\"BrBG\",annot=True)"
      ],
      "id": "f78689e9",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66518066"
      },
      "source": [
        "# Hypothesis "
      ],
      "id": "66518066"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e13ca6ba"
      },
      "source": [
        "## 1 T test\n",
        "\n",
        "The two sample t-test is also known as the independent samples, independent, and unpaired t-test. Moreover, this type of statistical test compares two averages (means) and will give you information if these two means are statistically different from each other. The t-test also tells you whether the differences are statistically significant. In other words it lets you know if those differences could have happened by chance.\n",
        "\n",
        "Now, when performing t-tests you typically have the following two hypotheses:\n",
        "\n",
        "    Null hypotheses: Two group means are equal\n",
        "    Alternative hypotheses: Two group means are different (two-tailed)\n",
        "\n",
        "- Ho: µ EU cars == µ US cars\n",
        "- Ha: µ EU cars != µ US cars"
      ],
      "id": "e13ca6ba"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "816f8a9d"
      },
      "source": [
        "# Subset data\n",
        "EU_cars = df.query('Origin == \"Asia\"')['MSRP']\n",
        "US_cars = df.query('Origin == \"USA\"')['MSRP']\n"
      ],
      "id": "816f8a9d",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5459860b"
      },
      "source": [
        "from scipy import stats\n",
        "rng = np.random.default_rng()"
      ],
      "id": "5459860b",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "011e7716",
        "outputId": "8fc239e0-4071-40e6-c1be-29394205e24b"
      },
      "source": [
        "res = stats.ttest_ind(EU_cars, US_cars, \n",
        "                      equal_var=True)\n",
        "\n",
        "display(res)"
      ],
      "id": "011e7716",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-1.1743283738395427, pvalue=0.2414727243778856)"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b52bc9c"
      },
      "source": [
        "__Conclusion:__ Our results show that the difference between EU cars and USA cars is not statistically significant.  It is safe to say that we fail to reject null hypotesis. Although there seems to be a difference on the grapgh, this variation is not statistically significant.  I also checked the results without removing outliers and the outliers affect this test and suggest that EU cars are expensive, as explained above."
      ],
      "id": "0b52bc9c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4092e3e3",
        "outputId": "40360ce1-7ee8-4d10-b5d3-5602873470ca"
      },
      "source": [
        "# Box plot for the origins of cars\n",
        "sns.boxplot(x= 'Origin', y='MSRP',  data= df)"
      ],
      "id": "4092e3e3",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Origin', ylabel='MSRP'>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c49b52b6"
      },
      "source": [
        "## Hypothesis 2\n",
        "\n",
        "The second hypothesis is based on the assumption that rear drive cars are expensive than front drive cars. \n",
        "\n",
        "Ho: µ Rear drive car == µ Front drive car  \n",
        "Ha: µ Rear drive train car != µ Front drive car"
      ],
      "id": "c49b52b6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0834a3dd"
      },
      "source": [
        "# Subset data\n",
        "rear = df.query('DriveTrain == \"Rear\"')['MSRP']\n",
        "front = df.query('DriveTrain == \"Front\"')['MSRP']\n",
        "\n"
      ],
      "id": "0834a3dd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08e5d5b1",
        "outputId": "1935d949-7473-4538-a594-474755746939"
      },
      "source": [
        "res1 = stats.ttest_ind(rear, front,  random_state=rng,\n",
        "                      equal_var=True)\n",
        "\n",
        "display(res1)"
      ],
      "id": "08e5d5b1",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=8.693002196121649, pvalue=4.2414353450674266e-16)"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34f4e965"
      },
      "source": [
        "__Results:__  Our test results show that p value of the analysis is statistically significant, which means that we reject the null hypothesis. As shown below, the price of rear drive cars expensive because it is known that most of the sport adn luxury cars have rear drive. "
      ],
      "id": "34f4e965"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4f7542a1",
        "outputId": "2f004a84-4e5c-4515-d173-e9c9c38dc83c"
      },
      "source": [
        "# df.DriveTrain.value_counts()\n",
        "sns.boxplot(x= 'DriveTrain', y= 'MSRP', data= df)"
      ],
      "id": "4f7542a1",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='DriveTrain', ylabel='MSRP'>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8e2ade11"
      },
      "source": [
        "## Hypothesis 3\n",
        "\n",
        "Let's finally check out whether the price of Ford cars are expensive than  Toyota cars. \n",
        "\n",
        "Ho: µ Ford == µ Toyota  \n",
        "Ha: µ Ford != µ Toyota\n",
        "\n"
      ],
      "id": "8e2ade11"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c91e8953"
      },
      "source": [
        "# Subset data\n",
        "ford = df.query('Make == \"Ford\"')['MSRP']\n",
        "toyota = df.query('Make == \"Toyota\"')['MSRP']"
      ],
      "id": "c91e8953",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "468d9d3f",
        "outputId": "a8d85d40-be98-481a-fc45-b6897f406ddd"
      },
      "source": [
        "res2 = stats.ttest_ind(rear, front, \n",
        "                      equal_var=True)\n",
        "\n",
        "display(res2)"
      ],
      "id": "468d9d3f",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=8.693002196121649, pvalue=4.2414353450674266e-16)"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cdee960"
      },
      "source": [
        "__Results:__ Analysis results show that the price of two car companies in our data set is also statistically significant. The p value indicates that we reject the null hypothesis. The difference can be seen below. Since we introduced the Ford first, the outout is -8.693002196121649, which means that Ford cars are that much cheaper than Toyota on average. "
      ],
      "id": "5cdee960"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fb1eb3b",
        "outputId": "d6466d4b-d209-4195-e238-7f24b76eb5ba"
      },
      "source": [
        "cars = ['Ford', 'Toyota']\n",
        "df2= df[df.Make.isin(cars)]\n",
        "\n",
        "\n",
        "df2.head()\n",
        "\n",
        "sns.boxplot(x= 'Make', y= 'MSRP', data= df2)"
      ],
      "id": "6fb1eb3b",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Make', ylabel='MSRP'>"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEJCAYAAABR4cpEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfbklEQVR4nO3df3BU1f3/8edmd41gaCA0izSmcbRWZWhh2qBEmUQ+lk1gWUISqUJKqu3IDxV0OmUIkAwFkaKmoAiB0aG2olZSqEmMywLFEUajQkHFWPydhB/B/IAksKFJNpv9/sG3qym6gDebheT1mGGGPXvv7vuEG157z917jsnv9/sRERExICLcBYiIyKVPYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMs4S7gHBpbGyhs1O32IiInI+ICBODBl3xrc/32TDp7PQrTEREuomGuURExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhffarwb3Bm2/u5o03doW1hubmJgCioweGtQ6AMWNSuPXW5HCXIdIn6cxEDGlubqa5uTncZYhImJn66kqLx497dNNiN3j00YcBmD8/P8yViEgoRUSYGDw46tuf78FaRESkl1KYiIiIYQoTERExTGEiIiKGKUxERMQwhYmIiBimMBEREcMUJiIiYpjCREREDFOYiIiIYQoTERExTLMGi0i304zWXfWFGa11ZiIivZJmtO5ZOjMRkW53663JYf8krhmte1ZIz0yefPJJJkyYgMPh4NlnnwVgwYIF2O120tPTSU9PZ8eOHQCUl5fjdDqx2+2sWrUq8BoHDx4kKyuL1NRUFi1aREdHBwA1NTVkZ2eTlpbG7NmzaWlpCWVXREQkiJCFyZ49e3j77bcpLS1ly5YtbNy4kS+++IKKigqef/55SkpKKCkpYdy4cbS2trJw4UIKCwtxuVxUVFSwa9eZ8dZ58+aRn5/Ptm3b8Pv9FBUVAbBkyRKmTZuG2+1m+PDhFBYWhqorIiJyDiELk5tuuonnnnsOi8XC8ePH8fl8REZGUlNTQ35+Pk6nk9WrV9PZ2cmBAwdISEggPj4ei8WC0+nE7XZz9OhRWltbGTlyJACZmZm43W68Xi979+4lNTW1S7uIiIRHSIe5rFYrq1evxuFwkJSUhM/nY/To0SxfvpyioiL+9a9/sXnzZurq6oiNjQ3sZ7PZqK2tPas9NjaW2tpaGhsbiYqKwmKxdGkXEZHwCPkF+Llz53Lvvfcya9Ys3nrrLdauXRt4bvr06RQXF5OWlnbWfiaTiW9aUThY+4UItvyknD+r1QxAbOyAMFci0pWOzZ4VsjD5/PPPaW9v58Ybb6Rfv37Y7XZcLhcDBw4MDE/5/X4sFgtDhgyhoaEhsG9dXR02m+2s9vr6emw2GzExMXg8Hnw+H2azOdB+IbQGfPfwen0A1NefCnMlIl3p2OxeYVsD/siRI+Tl5dHe3k57ezs7d+5k1KhRLF++nObmZrxeL5s2bWLcuHGMGDGCyspKqqur8fl8lJWVkZycTFxcHJGRkezbtw+A4uJikpOTsVqtJCYm4nK5urSLiEh4hOzMJCUlhffff5/JkydjNpux2+088MADDBo0iKlTp9LR0YHdbmfixIkArFixgjlz5tDW1kZKSkpg6KugoIC8vDxaWloYNmwYOTk5ACxevJjc3FzWrVvH0KFDWblyZai6IiIi52Dyf9MFiD5Aw1zdQzeGycVKx2b3Ctswl4iI9B0KExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImKYwkRERAxTmIiIiGEKExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImKYwkRERAxTmIiIiGEKExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImKYwkRERAxTmIiIiGEKExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImJYSMPkySefZMKECTgcDp599lkAysvLcTqd2O12Vq1aFdj24MGDZGVlkZqayqJFi+jo6ACgpqaG7Oxs0tLSmD17Ni0tLQCcPHmSGTNmMH78eLKzs6mvrw9lV0REJIiQhcmePXt4++23KS0tZcuWLWzcuJGPPvqIhQsXUlhYiMvloqKigl27dgEwb9488vPz2bZtG36/n6KiIgCWLFnCtGnTcLvdDB8+nMLCQgCeeOIJEhMT2bp1K1OmTOGRRx4JVVdEROQcQhYmN910E8899xwWi4Xjx4/j8/k4efIkCQkJxMfHY7FYcDqduN1ujh49SmtrKyNHjgQgMzMTt9uN1+tl7969pKamdmkHeP3113E6nQBMnDiR3bt34/V6Q9UdEREJIqTDXFarldWrV+NwOEhKSqKuro7Y2NjA8zabjdra2rPaY2Njqa2tpbGxkaioKCwWS5d2oMs+FouFqKgoTpw4EcruiIjIt7CE+g3mzp3Lvffey6xZs6iqqjrreZPJhN/vv6D2bxMRcf7ZOHhw1HlvK9/OajUDEBs7IMyViHSlY7NnhSxMPv/8c9rb27nxxhvp168fdrsdt9uN2WwObFNXV4fNZmPIkCE0NDQE2uvr67HZbMTExODxePD5fJjN5kA7nDmraWho4Morr6SjowOPx8PAgQPPu77jxz10dp4dVnJhvF4fAPX1p8JciUhXOja7V0SEKeiH8JANcx05coS8vDza29tpb29n586d3HXXXVRWVlJdXY3P56OsrIzk5GTi4uKIjIxk3759ABQXF5OcnIzVaiUxMRGXy9WlHSAlJYXi4mIAXC4XiYmJWK3WUHVHRESCCNmZSUpKCu+//z6TJ0/GbDZjt9txOBzExMQwZ84c2traSElJIS0tDYCCggLy8vJoaWlh2LBh5OTkALB48WJyc3NZt24dQ4cOZeXKlQA8+OCD5Obm4nA4GDBgAAUFBaHqioiInIPJ/00XJvoADXN1j0cffRiA+fPzw1yJSFc6NrtX2Ia5RESk71CYiIiIYQoTERExTGEiIiKGKUxERMQwhYmIiBimMBEREcMUJiIiYpjCREREDFOYiIiIYQoTERExTHNzfQcvvvgchw9Xd3NFl6ZDh878HH74w4QwV3JxiI9PYNq0nLC9v47Nr+jY7MrosXmuublCvjhWb3T4cDUff/oZ5ssHhruUsOv0nVmf5rPDDefYsvfztTaFuwQOH66m6rOPuDJKv9r96QSg9cvPwlxJ+H3p6Qj5e+iI+47Mlw+kf8Lt4S5DLiKnq3eGuwQAroyycM9PY8JdhlxEnj0Q+iXNdc1EREQMU5iIiIhhChMRETFMYSIiIoadM0w6OztpamrqgVJERORSFTRM3nvvPZKTk0lKSmLSpElUV+v76yIicragYfLYY4/x8MMP8+6775KZmcmf/vSnnqpLREQuIUHD5PTp04wdO5bLL7+cu+++my+++KKn6hIRkUtI0DCJiOj6tNVqDWkxIiJyaQoaJn102i4REblAQadTOXz4MLNmzfrWx+vXrw9dZSJywZqbm2j0dPTI9Bly6fjS08Gg5qaQvkfQMFm0aFGXx6mpqSEtRkRELk1BwyQjI+Nbn3vzzTe7vRgRMSY6eiCR/2nQRI/SxbMHTnB59MCQvkfQayYffvghd911F7NmzeLEiTOnzTU1Ndx///3Mnj07pIWJiMilI2iY/OEPf8But3PVVVexbt06XC4XDoeD1tZWSkpKeqpGERG5yAUd5jp16hS/+c1v8Pl8pKamsnXrVpYtW4bD4eip+kRE5BIQNEz69esHgNlspq2tjWeeeYYbb7yxRwoTEZFLx3nfZxITE3PBQbJmzRocDgcOh4PHHnsMgAULFmC320lPTyc9PZ0dO3YAUF5ejtPpxG63s2rVqsBrHDx4kKysLFJTU1m0aBEdHWeWn6ypqSE7O5u0tDRmz55NS0vLBdUmIiLdJ2iYdHZ20tzcTFNTE36/P/D3//4Jpry8nDfeeIOXX36Z4uJiPvzwQ3bs2EFFRQXPP/88JSUllJSUMG7cOFpbW1m4cCGFhYW4XC4qKirYtWsXAPPmzSM/P59t27bh9/spKioCYMmSJUybNg23283w4cMpLCzsnp+IiIhcsKBh8sknnzB69GhGjx7NJ598ws033xx4nJSUFPSFY2Njyc3N5bLLLsNqtXLttddSU1NDTU0N+fn5OJ1OVq9eTWdnJwcOHCAhIYH4+HgsFgtOpxO3283Ro0dpbW1l5MiRAGRmZuJ2u/F6vezduzdw38t/20VEJDyCXjP56KOPvvMLX3fddYG/V1VV4XK5ePHFF9mzZw9Lly6lf//+zJw5k82bN9O/f39iY2MD29tsNmpra6mrq+vSHhsbS21tLY2NjURFRWGxWLq0i4hIeAQNEzhz3cTn82GxWPB4PJSXl3P99deTkJBwXm/w6aefMnPmTObPn88111zD2rVrA89Nnz6d4uJi0tLSztrPZDJ949xgwdovxODBURe0/ddZrebvvK/0blarmdjYAWF9/9awvbtczEJ9bAYNk88++4wZM2aQn59PUlISU6ZMwWQy0draytKlSxkzZkzQF9+3bx9z585l4cKFOBwOPv74Y6qqqgLDU36/H4vFwpAhQ2hoaAjsV1dXh81mO6u9vr4em81GTEwMHo8Hn8+H2WwOtF+I48c9dHZ+t4ksvV7fd9pPej+v10d9/amwvr/INzF6bEZEmIJ+CD/n4lgPPfQQY8eO5dVXXwWgrKyMF154gTVr1gR942PHjnH//fdTUFAQuC/F7/ezfPlympub8Xq9bNq0iXHjxjFixAgqKyuprq7G5/NRVlZGcnIycXFxREZGsm/fPgCKi4tJTk7GarWSmJiIy+Xq0i4iIuER9Mzk2LFjTJo0CYB33nmH22+/nYiICIYOHYrH4wn6whs2bKCtrY0VK1YE2u666y5mzJjB1KlT6ejowG63M3HiRABWrFjBnDlzaGtrIyUlJTD0VVBQQF5eHi0tLQwbNoycnBwAFi9eTG5uLuvWrWPo0KGsXLnyu/8URETEkKBh8vXFsd59913y8vICj9va2oK+cF5eXpftvy47O/ustqSkJEpLS89qv+GGG9i8efNZ7XFxcWzcuDFoDSIi0jOChkl0dDQfffQRHo+H+vp6Ro0aBcD+/fsZMmRIjxQoIiIXv6Bh8rvf/Y67774bj8fD73//e/r378+GDRtYv359l29liYhI3xY0TK6++mpeffVVTCYTERERNDU1MWLECP785z8THx/fUzWKiMhFLmiYjB49usv9G1+/v8NkMnHw4MHQVSYiIpeMc660uH//fv7v//6PrKwsfvSjH/VUXSIicgkJGiZ//OMf+c9//sP27dt55JFHOH36NJMmTcLpdPK9732vp2oUEZGL3DmnU+nXr19guvgvv/ySkpIScnJyuPrqq3niiSd6oEQREbnYBb0D/n+dOHGCEydO0NjYyKlT4ZsyQkRELi7nPDM5duwYpaWllJaWEhERwaRJkygqKtJ9JiIiEhA0TKZPn05lZSUTJkzg8ccfZ9iwYT1Vl4iIXEKChsnevXuJjIzk73//e5cpTfx+PyaTif3794e8QBERufgFDZOdO3f2VB0iInIJCxomcXFxPVWHiIhcwi7o21wiIiLfRGEiIiKGnfOrwXK25uYmfK1NnK7WNSX5iq+1ieZm/UpJ36QzExERMUwfo76D6OiB1J/soH/C7eEuRS4ip6t3Eh09MNxl8KWng2cPnAh3GWHnae8EIOoyfWb+0tPB1SF+D4WJSC8SH58Q7hIuGnWHqgH4/pX6mVxN6I8NhYlILzJtWk64S7hoPProwwDMn58f5kr6Bp3/iYiIYQoTERExTGEiIiKGKUxERMQwhYmIiBimMBEREcMUJiIiYpjCREREDFOYiIiIYQoTERExTGEiIiKGhTRM1qxZg8PhwOFw8NhjjwFQXl6O0+nEbrezatWqwLYHDx4kKyuL1NRUFi1aREdHBwA1NTVkZ2eTlpbG7NmzaWlpAeDkyZPMmDGD8ePHk52dTX19fSi7IiIiQYQsTMrLy3njjTd4+eWXKS4u5sMPP6SsrIyFCxdSWFiIy+WioqKCXbt2ATBv3jzy8/PZtm0bfr+foqIiAJYsWcK0adNwu90MHz6cwsJCAJ544gkSExPZunUrU6ZM4ZFHHglVV0RE5BxCFiaxsbHk5uZy2WWXYbVaufbaa6mqqiIhIYH4+HgsFgtOpxO3283Ro0dpbW1l5MiRAGRmZuJ2u/F6vezdu5fU1NQu7QCvv/46TqcTgIkTJ7J79268Xm+ouiMiIkGELEyuu+66QDhUVVXhcrkwmUzExsYGtrHZbNTW1lJXV9elPTY2ltraWhobG4mKisJisXRpB7rsY7FYiIqK4sQJLQgkIhIOIV/P5NNPP2XmzJnMnz8fi8VCZWVll+dNJhN+v/+s/YK1f5uIiPPPxsGDo8572/9ltZq/877Su1mtZmJjB4S7DOGr31P9e/SMkIbJvn37mDt3LgsXLsThcLBnzx4aGhoCz9fV1WGz2RgyZEiX9vr6emw2GzExMXg8Hnw+H2azOdAOZ85qGhoauPLKK+no6MDj8TBw4MDzru34cQ+dnWeH1fnwen3faT/p/bxeH/X1p8JdhvDV76n+PbpHRIQp6IfwkIXJsWPHuP/++1m1ahVJSUkAjBgxgsrKSqqrq7nqqqsoKysjKyuLuLg4IiMj2bdvHz//+c8pLi4mOTkZq9VKYmIiLpcLp9MZaAdISUmhuLiYWbNm4XK5SExMxGq1hqo7InIB3nxzN2+8sSusNRz6/8v2/nfFxXAaMyaFW29NDncZIRWyMNmwYQNtbW2sWLEi0HbXXXexYsUK5syZQ1tbGykpKaSlpQFQUFBAXl4eLS0tDBs2jJycM8uPLl68mNzcXNatW8fQoUNZuXIlAA8++CC5ubk4HA4GDBhAQUFBqLoiIpeg6OjocJfQp5j833Rhog8wMsz16KMP89nhBvon3N7NVcml7HT1Tn4U/32tOS690rmGuXQHvIiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERw0K2Bnxv52tt4nT1znCXEXadHa0ARFguD3Ml4edrbQK+H+4yRMJCYfIdxMcnhLuEi8ahQ9UA/DBe/4nC93VsSJ9l8vv9/nAXEQ7Hj3vo7OyTXe9Wjz76MADz5+eHuRIRCaWICBODB0d9+/M9WIuIiPRSChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETEs5GHi8XiYOHEiR44cAWDBggXY7XbS09NJT09nx44dAJSXl+N0OrHb7axatSqw/8GDB8nKyiI1NZVFixbR0dEBQE1NDdnZ2aSlpTF79mxaWlpC3RUREfkWIQ2T999/n6lTp1JVVRVoq6io4Pnnn6ekpISSkhLGjRtHa2srCxcupLCwEJfLRUVFBbt27QJg3rx55Ofns23bNvx+P0VFRQAsWbKEadOm4Xa7GT58OIWFhaHsioiIBBHSMCkqKmLx4sXYbDYATp8+TU1NDfn5+TidTlavXk1nZycHDhwgISGB+Ph4LBYLTqcTt9vN0aNHaW1tZeTIkQBkZmbidrvxer3s3buX1NTULu0iIhIeIZ2C/pFHHuny+Pjx44wePZqlS5fSv39/Zs6cyebNm+nfvz+xsbGB7Ww2G7W1tdTV1XVpj42Npba2lsbGRqKiorBYLF3aRUQkPHp0PZP4+HjWrl0beDx9+nSKi4tJS0s7a1uTycQ3zY4frP1CBJtKWc6f1WoGIDZ2QJgrEZFw6tEw+fjjj6mqqgoMT/n9fiwWC0OGDKGhoSGwXV1dHTab7az2+vp6bDYbMTExeDwefD4fZrM50H4htJ5J9/B6fQDU158KcyUiEkoX1Xomfr+f5cuX09zcjNfrZdOmTYwbN44RI0ZQWVlJdXU1Pp+PsrIykpOTiYuLIzIykn379gFQXFxMcnIyVquVxMREXC5Xl3YREQmPHj0zueGGG5gxYwZTp06lo6MDu93OxIkTAVixYgVz5syhra2NlJSUwNBXQUEBeXl5tLS0MGzYMHJycgBYvHgxubm5rFu3jqFDh7Jy5cqe7IqIiHyNlu0VQ7Rsr0jfcFENc4mISO+kMBEREcMUJiIiYpjCREREDFOYiIiIYQoTERExTGEiIiKGKUxERMQw3bR4CXvzzd288causNZw6FA1AD/8YUJY6wAYMyaFW2/VtDpyRlNTI+vXP8Xs2XOJjh4Y7nIuebppUUIqOjqa6OjocJchcpZXXnmZTz/9mNLSf4S7lD5BZyYi0us0NTUyf/5DeL1erNbLeOyxJ3R2YpDOTESkz3nllZcDHxY7Ozt1dtIDFCYi0uu89dab+HwdAPh8Hbz11pthrqj3U5iISK+TlHQrZvOZFTbMZgtJSbeGuaLeT2EiIr2O05lBRMSZpbwjIiKYNCkzzBX1fgoTEel1Bg4cxJgxKZhMJsaMSdbF9x7Qoystioj0FKczg6NHj+ispIfoq8EiInJO+mqwiIiEnMJEREQMU5iIiIhhffYC/H+/NigiIud2rv8z++wFeBER6T4a5hIREcMUJiIiYpjCREREDFOYiIiIYQoTERExTGEiIiKGKUxERMQwhYmIiBimMBEREcP67HQqcm5HjhwhLS2Na6+9tkv7+vXrGTp06AW/Vk5ODq+99lp3lii93JIlS9i/fz9er5dDhw4FjsWcnByysrIMvfaCBQt44IEHiIuL645S+zyFiQRls9koKSkJdxnSRy1evBj46sNIdx6L77zzDvfff3+3vV5fp2EuuWANDQ3MnDkTp9NJRkYGu3fvBuCpp57it7/9LRMmTOCFF17g3//+NxkZGWRkZLB27dowVy29RWVlJdOnT8fpdHLnnXdy4MABPB4PN998Mx6PBzgTPg6HA4AtW7YwceJEnE4nubm5tLS08PTTT1NXV8eMGTNobGxk69at/PKXv2TSpEmkpqayd+/ecHbxkqQzEwmqrq6O9PT0wGOn08kHH3zA6NGjueeeezh8+DBTp06luLgYgPb2dlwuV2DbBQsWcMstt7B27VreeeedcHRBepl58+YxY8YM7HY77733Hg8++CDbtm3jtttuw+12c8cdd1BcXEx6ejoff/wx69evp6ioiEGDBrFkyRLWrFnD/Pnzeemll3j66aeJjo7mpZdeYv369cTExLB582Y2bNjAqFGjwt3VS4rCRIL6pmGum2++mWXLlgEQHx/PiBEjeP/99wH46U9/CsCJEyeoq6vjlltuASAzM5MtW7b0YOXSG7W0tHDo0CHsdjsAI0eOJDo6mi+++IKsrCyeeuop7rjjDsrKyvjrX//Kjh07GDt2LIMGDQLgzjvvZMGCBV1eMyIigrVr1/Laa69RWVnJnj17iIjQoM2F0k9MLtj/rlrg9/vx+XwAXH755QCYTKYu25nN5p4rUHotv9//rcffqFGjqKurY/v27Vx11VUMGTKEzs7Os7bt6Ojo0tbS0kJWVhZHjhxh1KhRTJ8+PeT96I0UJnLBRo8ezebNmwE4fPgw+/fvZ+TIkV22GTRoED/4wQ94/fXXASgrK+vhKqU3ioqKIj4+nu3btwPw3nvv0dDQwHXXXYfJZGLy5MksW7aMzMxMAG666SZee+01mpqaACgqKuLmm28GznzA8fl8VFVVERERwaxZsxg9ejS7d+8OfDiS86cwkQu2aNEi3n77bZxOJ/fddx/Lli3DZrOdtd3jjz/OmjVrmDx5MocOHQpDpdIbPf7442zcuBGn08nSpUt56qmnuOyyywBwOBy0trbyi1/8AoAbbriBmTNnMn36dNLS0jh58iQPPfQQALfddhszZsxgwIAB3HjjjYwfP56MjAz69+9PTU1NuLp3ydJKiyLSK3R2dvK3v/2NyspK8vLywl1On6ML8CLSKzzwwAMcO3aMDRs2hLuUPklnJiIiYpiumYiIiGEKExERMUxhIiIihilMRELkyJEjXH/99WRnZ5/13IIFC7j++us5ceLEt+6fm5uri8lyyVCYiIRQZGQkVVVVHD16NNB2+vRp9u3bF8aqRLqfwkQkhMxmM+PHj+eVV14JtG3fvp3bb78dODO9x7Jly5gyZQoTJkxg/Pjx3xg0f/zjH/n1r39NS0sL7e3tLF++nIyMDCZNmkRubm5gtlyRcFGYiITY5MmTKS0tDTwuLi4mIyMDODOdel1dHZs2bcLlcpGRkcEzzzwT2Nbv97NkyRKOHj3KM888wxVXXMHTTz+N2WzmH//4B6WlpdhsNgoKCnq8XyJfp5sWRUJs+PDhREREUFFRweDBg2lpaeHHP/4xANdccw0PPfQQL730EocPH+add97hiiuuCOz7l7/8hePHj1NcXByYMuT111/n1KlTlJeXA+D1ehk8eHDPd0zkaxQmIj1g0qRJlJaWEhMT02V9mF27dlFYWMg999zD7bffzjXXXNPlLGbUqFH87Gc/Y8GCBWzatAmr1UpnZycLFy4kJSUFODPrbVtbW4/3SeTrNMwl0gPS09Nxu924XC4mTpwYaP/ggw8YO3Ys06ZN4yc/+Qn//Oc/u8xYO3z4cH71q18xYMAA1qxZA8CYMWN44YUXaG9vp7Ozk/z8fFauXNnjfRL5OoWJSA8YMmQI1157LVdffTUDBw4MtE+YMIG9e/cGlqCNj4/nyJEjXdbhMJlMLF++nBdffJH9+/dz3333ERcXR0ZGBhMmTMDv95ObmxuGXol8RXNziYiIYTozERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGPb/AMlYucXYXMuIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "104af4a0"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "In this post, I illustrated how to make an EDA analysis for car dataset and how to create hypothesis testing models.\n",
        "\n",
        "Briefly:\n",
        "1. I converted __chr__ format variables into numbers. To do so, I first removed some chacracters from the data and then changed the data type.\n",
        "2. I replaced missing variable with the set median. \n",
        "3. I identified outliers and then remove some of them. I also prepared some codes for data normalization procedure. \n",
        "4. I created three graphs about the dataset. \n",
        "5. I created three hypothesis testing models and used ttest to compare groups means. \n",
        "6. I also created boxplots to show the ttest results in context. \n",
        "\n",
        "# Further steps\n",
        "\n",
        "The data is now ready for machine learning models. Before using them directly, we will apply normalization procedure as examplified above, including all variables.\n",
        "\n",
        "Moreover, all categorical variables need to be converted to dummy variables. \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "__Thanks for your visit and reading!__"
      ],
      "id": "104af4a0"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci5GWY67UFMR"
      },
      "source": [
        ""
      ],
      "id": "ci5GWY67UFMR",
      "execution_count": null,
      "outputs": []
    }
  ]
}