week 3/SA2021_W3_solutions.ipynb

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   "source": [
    "<br>\n",
    "<h1 style = \"font-size:40px; font-family:monospace ; font-weight : bold; color : #3C0096; text-align: center; border-radius: 5px 25px;\">SA-2021 Week 3 Graded Assignment [Solved]</h1>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vziCwtYSvpav"
   },
   "source": [
    "<br>\n",
    "<h1 style = \"font-size:30px; font-family:monospace ; font-weight : bold; color : #AE0052; text-align: center; border-radius: 10px 10px;\">☔Rain Prediction in Australia☔</h1>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qrhh_RHuvpav"
   },
   "source": [
    "![](https://media0.giphy.com/media/tkMeNtj9FQYx2/giphy.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRQUWnirvpaw"
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   "source": [
    "<span style=\"font-size:20px;color:#0026D8;font-weight : bold\">Dataset Description:</span>\n",
    "\n",
    "**This dataset contains daily weather observations from numerous Australian weather stations.**\n",
    "- **Date** - The date of observation<br>\n",
    "- **Location** - The common name of the location of the weather station<br>\n",
    "- **MinTemp** - The minimum temperature in degrees celsius<br>\n",
    "- **MaxTemp** - The maximum temperature in degrees celsius<br>\n",
    "- **Rainfall** - The amount of rainfall recorded for the day in mm<br>\n",
    "- **Evaporation** - The so-called Class A pan evaporation (mm) in the 24 hours to 9am<br>\n",
    "- **Sunshine** - The number of hours of bright sunshine in the day.<br>\n",
    "- **WindGustDir** - The direction of the strongest wind gust in the 24 hours to midnight<br>\n",
    "- **WindGustSpeed** - The speed (km/h) of the strongest wind gust in the 24 hours to midnight<br>\n",
    "- **WindDir9am** - Direction of the wind at 9am<br>\n",
    "- **WindDir3pm** - Direction of the wind at 3pm<br>\n",
    "- **WindSpeed9am** - Wind speed (km/hr) averaged over 10 minutes prior to 9am<br>\n",
    "- **WindSpeed3pm** - Wind speed (km/hr) averaged over 10 minutes prior to 3pm<br>\n",
    "- **Humidity9am** - Humidity (percent) at 9am<br>\n",
    "- **Humidity3pm** - Humidity (percent) at 3pm<br>\n",
    "- **Pressure9am** - Atmospheric pressure (hpa) reduced to mean sea level at 9am<br>\n",
    "- **Pressure3pm** - Atmospheric pressure (hpa) reduced to mean sea level at 3pm<br>\n",
    "- **Cloud9am** - Fraction of sky obscured by cloud at 9am. This is measured in \"oktas\", which are a unit of                    eigths. It records how many eigths of the sky are obscured by cloud. A 0 measure indicates                    completely clear sky whilst an 8 indicates that it is completely overcast.<br>\n",
    "- **Cloud3pm** - Fraction of sky obscured by cloud (in \"oktas\": eighths) at 3pm. See Cload9am for a description                of the values<br>\n",
    "- **Temp9am** - Temperature (degrees C) at 9am<br>\n",
    "- **Temp3pm** - Temperature (degrees C) at 3pm<br>\n",
    "- **RainToday** - Boolean: 1 if precipitation (mm) in the 24 hours to 9am exceeds 1mm, otherwise 0<br>\n",
    "- **RainTomorrow** - The target variable. Will it rain tomorrow?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rsH1_6fPvpaw"
   },
   "source": [
    "### It is highly recommended to read the documentation before implementing any alogorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tEcIZ5qdvpax"
   },
   "source": [
    "- **Logistic Regression** : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html <br>\n",
    "- **SVM** : https://scikit-learn.org/stable/modules/svm.html <br>\n",
    "- **Confusion Matrix** : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html <br>\n",
    "- **Plot Confusion Matrix** : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html <br>\n",
    "- **ROC_AUC Score** : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score <br>\n",
    "- **Classification Report** : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html <br>\n",
    "- **Label Encoder** : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html <br>\n",
    "- **Label Binarizer** : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html <br>\n",
    "- **Standard Scaler** : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html <br>\n",
    "- **Cross Val Score** : https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html <br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1_Q5m8Xmvpax",
    "outputId": "1acb512f-6742-4e5b-e337-aca067972937"
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   "outputs": [
    {
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   "source": [
    "## ======== Run this Cell as it is======== ##\n",
    "\n",
    "from IPython.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    "h1,h2,h3 {\n",
    "\tmargin: 1em 0 0.5em 0;\n",
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    "\tborder-radius: 0 10px 0 15px;\n",
    "\tbackground: #fff\n",
    "    \n",
    "}\n",
    "</style>\n",
    "\"\"\")\n",
    "\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQG2K5CQvpaz"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">✅ Importing Required Libraries</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "DHyUvB8tvpa0"
   },
   "outputs": [],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Import numpy, pandas, matplotlib, seaborn\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "## Import Logistic regression and SVC\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "## Import Confusion matrix, plot confusion matrix, classification report, accuracy score, auc-roc score and roc curve\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "## Import Label encoder, label binarizer and Standard Scaler\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder,LabelBinarizer\n",
    "\n",
    "## ================================ ##\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r-tHNg8qvpa0"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Read the data </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "m-owrU7vvpa0"
   },
   "outputs": [
    {
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       "\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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2856</td>\n",
       "      <td>11</td>\n",
       "      <td>18.3</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>1010.7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>575</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2037</td>\n",
       "      <td>6</td>\n",
       "      <td>11.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>88</td>\n",
       "      <td>1008.4</td>\n",
       "      <td>1004.7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>19</td>\n",
       "      <td>1.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>64</td>\n",
       "      <td>1018.4</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2895</td>\n",
       "      <td>18</td>\n",
       "      <td>23.7</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>50</td>\n",
       "      <td>1014.2</td>\n",
       "      <td>1010.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2856        11     18.3     35.4       0.0         10.7       8.2   \n",
       "1   575         0     12.0     21.1       0.0          2.4       4.9   \n",
       "2  2037         6     11.9     17.0       1.6          5.6       0.8   \n",
       "3  2010        19      1.3     10.2       4.2          2.6       4.4   \n",
       "4  2895        18     23.7     31.5       0.0         11.0      12.1   \n",
       "\n",
       "   WindGustDir  WindGustSpeed  WindDir9am  ...  Humidity9am  Humidity3pm  \\\n",
       "0           15             31           0  ...           17           14   \n",
       "1           11             22          15  ...           70           60   \n",
       "2            5             48           4  ...           71           88   \n",
       "3           15             33          15  ...           87           64   \n",
       "4            4             35           2  ...           53           50   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1012.8       1010.7         3         7     26.3     34.0          0   \n",
       "1       1022.3       1020.2         2         7     16.2     19.4          0   \n",
       "2       1008.4       1004.7         7         7     14.6     13.3          1   \n",
       "3       1018.4       1016.8         7         7      3.8      8.5          1   \n",
       "4       1014.2       1010.2         3         1     29.1     30.4          0   \n",
       "\n",
       "   RainTomorrow  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "df=pd.read_csv(r\"C:\\Users\\hp\\Downloads\\final_data.csv\")\n",
    "df.head()\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "mxownAh7vpa1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape ->  (24196, 23)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24196 entries, 0 to 24195\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Date           24196 non-null  int64  \n",
      " 1   Location       24196 non-null  int64  \n",
      " 2   MinTemp        24196 non-null  float64\n",
      " 3   MaxTemp        24196 non-null  float64\n",
      " 4   Rainfall       24196 non-null  float64\n",
      " 5   Evaporation    24196 non-null  float64\n",
      " 6   Sunshine       24196 non-null  float64\n",
      " 7   WindGustDir    24196 non-null  int64  \n",
      " 8   WindGustSpeed  24196 non-null  int64  \n",
      " 9   WindDir9am     24196 non-null  int64  \n",
      " 10  WindDir3pm     24196 non-null  int64  \n",
      " 11  WindSpeed9am   24196 non-null  int64  \n",
      " 12  WindSpeed3pm   24196 non-null  int64  \n",
      " 13  Humidity9am    24196 non-null  int64  \n",
      " 14  Humidity3pm    24196 non-null  int64  \n",
      " 15  Pressure9am    24196 non-null  float64\n",
      " 16  Pressure3pm    24196 non-null  float64\n",
      " 17  Cloud9am       24196 non-null  int64  \n",
      " 18  Cloud3pm       24196 non-null  int64  \n",
      " 19  Temp9am        24196 non-null  float64\n",
      " 20  Temp3pm        24196 non-null  float64\n",
      " 21  RainToday      24196 non-null  int64  \n",
      " 22  RainTomorrow   24196 non-null  int64  \n",
      "dtypes: float64(9), int64(14)\n",
      "memory usage: 4.2 MB\n"
     ]
    }
   ],
   "source": [
    "## Check the shape of data. It should be (24196,23)\n",
    "\n",
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "print(\"Shape -> \",df.shape)\n",
    "df.info()\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "sMBhA4H3vpa1"
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       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "      <td>24196.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1702.554430</td>\n",
       "      <td>11.126880</td>\n",
       "      <td>13.281232</td>\n",
       "      <td>23.124078</td>\n",
       "      <td>3.008857</td>\n",
       "      <td>5.114329</td>\n",
       "      <td>6.700591</td>\n",
       "      <td>7.752273</td>\n",
       "      <td>42.371880</td>\n",
       "      <td>7.315424</td>\n",
       "      <td>...</td>\n",
       "      <td>69.505621</td>\n",
       "      <td>55.335345</td>\n",
       "      <td>1016.774682</td>\n",
       "      <td>1014.508518</td>\n",
       "      <td>4.767152</td>\n",
       "      <td>4.926682</td>\n",
       "      <td>17.609216</td>\n",
       "      <td>21.493420</td>\n",
       "      <td>0.293396</td>\n",
       "      <td>0.453918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>837.645705</td>\n",
       "      <td>6.397324</td>\n",
       "      <td>6.084032</td>\n",
       "      <td>6.681013</td>\n",
       "      <td>8.399269</td>\n",
       "      <td>3.549384</td>\n",
       "      <td>3.958287</td>\n",
       "      <td>4.788052</td>\n",
       "      <td>14.221336</td>\n",
       "      <td>4.634019</td>\n",
       "      <td>...</td>\n",
       "      <td>17.671186</td>\n",
       "      <td>20.907040</td>\n",
       "      <td>6.987625</td>\n",
       "      <td>6.877345</td>\n",
       "      <td>2.741416</td>\n",
       "      <td>2.586083</td>\n",
       "      <td>6.189396</td>\n",
       "      <td>6.535474</td>\n",
       "      <td>0.455328</td>\n",
       "      <td>0.497882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.700000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>980.500000</td>\n",
       "      <td>979.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>984.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1012.200000</td>\n",
       "      <td>1009.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.700000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1651.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>1016.800000</td>\n",
       "      <td>1014.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2381.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1021.400000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3415.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>46.800000</td>\n",
       "      <td>206.200000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1040.200000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>39.400000</td>\n",
       "      <td>46.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Date      Location       MinTemp       MaxTemp      Rainfall  \\\n",
       "count  24196.000000  24196.000000  24196.000000  24196.000000  24196.000000   \n",
       "mean    1702.554430     11.126880     13.281232     23.124078      3.008857   \n",
       "std      837.645705      6.397324      6.084032      6.681013      8.399269   \n",
       "min        0.000000      0.000000     -4.700000      4.100000      0.000000   \n",
       "25%      984.000000      6.000000      8.600000     17.900000      0.000000   \n",
       "50%     1651.000000     11.000000     13.100000     22.700000      0.000000   \n",
       "75%     2381.000000     17.000000     18.000000     28.100000      1.800000   \n",
       "max     3415.000000     22.000000     31.400000     46.800000    206.200000   \n",
       "\n",
       "        Evaporation      Sunshine   WindGustDir  WindGustSpeed    WindDir9am  \\\n",
       "count  24196.000000  24196.000000  24196.000000   24196.000000  24196.000000   \n",
       "mean       5.114329      6.700591      7.752273      42.371880      7.315424   \n",
       "std        3.549384      3.958287      4.788052      14.221336      4.634019   \n",
       "min        0.000000      0.000000      0.000000       9.000000      0.000000   \n",
       "25%        2.400000      3.400000      3.000000      31.000000      3.000000   \n",
       "50%        4.400000      7.200000      8.000000      41.000000      7.000000   \n",
       "75%        7.000000     10.000000     12.000000      50.000000     11.000000   \n",
       "max       59.800000     14.500000     15.000000     122.000000     15.000000   \n",
       "\n",
       "       ...   Humidity9am   Humidity3pm   Pressure9am   Pressure3pm  \\\n",
       "count  ...  24196.000000  24196.000000  24196.000000  24196.000000   \n",
       "mean   ...     69.505621     55.335345   1016.774682   1014.508518   \n",
       "std    ...     17.671186     20.907040      6.987625      6.877345   \n",
       "min    ...      3.000000      0.000000    980.500000    979.000000   \n",
       "25%    ...     58.000000     41.000000   1012.200000   1009.900000   \n",
       "50%    ...     70.000000     56.000000   1016.800000   1014.400000   \n",
       "75%    ...     83.000000     70.000000   1021.400000   1019.000000   \n",
       "max    ...    100.000000    100.000000   1040.200000   1037.000000   \n",
       "\n",
       "           Cloud9am      Cloud3pm       Temp9am       Temp3pm     RainToday  \\\n",
       "count  24196.000000  24196.000000  24196.000000  24196.000000  24196.000000   \n",
       "mean       4.767152      4.926682     17.609216     21.493420      0.293396   \n",
       "std        2.741416      2.586083      6.189396      6.535474      0.455328   \n",
       "min        0.000000      0.000000     -0.700000      3.700000      0.000000   \n",
       "25%        2.000000      3.000000     12.700000     16.500000      0.000000   \n",
       "50%        6.000000      6.000000     17.300000     21.100000      0.000000   \n",
       "75%        7.000000      7.000000     22.300000     26.200000      1.000000   \n",
       "max        8.000000      9.000000     39.400000     46.100000      1.000000   \n",
       "\n",
       "       RainTomorrow  \n",
       "count  24196.000000  \n",
       "mean       0.453918  \n",
       "std        0.497882  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Apply describe on dataframe\n",
    "\n",
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "df.describe()\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0rh7Jr4tvpa1"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Check missing values in the dataset and fill them by any method</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "4z466pWsvpa1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date             0\n",
       "Location         0\n",
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "Evaporation      0\n",
       "Sunshine         0\n",
       "WindGustDir      0\n",
       "WindGustSpeed    0\n",
       "WindDir9am       0\n",
       "WindDir3pm       0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Cloud9am         0\n",
       "Cloud3pm         0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "RainToday        0\n",
       "RainTomorrow     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check if there any null value in the dataset.\n",
    "\n",
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "df.isnull().sum()\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-BNxcmrvpa2"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Use Feature Encoding :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "FZmvqTMxvpa2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24196, 23)\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2856</td>\n",
       "      <td>11</td>\n",
       "      <td>18.3</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>1010.7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>575</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2037</td>\n",
       "      <td>6</td>\n",
       "      <td>11.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>88</td>\n",
       "      <td>1008.4</td>\n",
       "      <td>1004.7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>19</td>\n",
       "      <td>1.3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>64</td>\n",
       "      <td>1018.4</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2895</td>\n",
       "      <td>18</td>\n",
       "      <td>23.7</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>50</td>\n",
       "      <td>1014.2</td>\n",
       "      <td>1010.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2856        11     18.3     35.4       0.0         10.7       8.2   \n",
       "1   575         0     12.0     21.1       0.0          2.4       4.9   \n",
       "2  2037         6     11.9     17.0       1.6          5.6       0.8   \n",
       "3  2010        19      1.3     10.2       4.2          2.6       4.4   \n",
       "4  2895        18     23.7     31.5       0.0         11.0      12.1   \n",
       "\n",
       "   WindGustDir  WindGustSpeed  WindDir9am  ...  Humidity9am  Humidity3pm  \\\n",
       "0           15             31           0  ...           17           14   \n",
       "1           11             22          15  ...           70           60   \n",
       "2            5             48           4  ...           71           88   \n",
       "3           15             33          15  ...           87           64   \n",
       "4            4             35           2  ...           53           50   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1012.8       1010.7         3         7     26.3     34.0          0   \n",
       "1       1022.3       1020.2         2         7     16.2     19.4          0   \n",
       "2       1008.4       1004.7         7         7     14.6     13.3          1   \n",
       "3       1018.4       1016.8         7         7      3.8      8.5          1   \n",
       "4       1014.2       1010.2         3         1     29.1     30.4          0   \n",
       "\n",
       "   RainTomorrow  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Use one hot encoding on dataframe\n",
    "\n",
    "## Optional step \n",
    "\n",
    "ohe = pd.get_dummies(data=df)\n",
    "print(ohe.shape)\n",
    "ohe.head()\n",
    "\n",
    "\n",
    "## ================================ ##\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pm9NZc9Bvpa2"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Split the data into train and test.</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ENos_-58vpa2"
   },
   "outputs": [],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    " \n",
    "## Test size should be 25% of the full dataset and use random state = 2021\n",
    "\n",
    "Y = df['RainTomorrow']\n",
    "X = df.drop(['RainTomorrow'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size=0.25, random_state=2021)\n",
    "\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "9z0dhowPvpa2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (18147, 22)\n",
      "X_test: (6049, 22)\n",
      "y_train: (18147,)\n",
      "y_test: (6049,)\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "   ## Let us print the shapes:\n",
    "    \n",
    "print(\"X_train:\", X_train.shape) # X_train\n",
    "print(\"X_test:\",X_test.shape) # X_test\n",
    "print(\"y_train:\",y_train.shape) # y_train\n",
    "print(\"y_test:\",y_test.shape) # y_test\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qgbT2Gqdvpa3"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Scale the data using Standard Scaler :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "3n7PsXYnvpa3"
   },
   "outputs": [],
   "source": [
    "## Use Standard Scaler to scale the train and test data\n",
    "\n",
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled= scaler.fit_transform(X_train)\n",
    "X_test_scaled= scaler.transform(X_test)\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IY8VC5sBvpa3"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Train a Logistic Regression with all default parameters</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "8vZV0RUhvpa3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=2021)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Use Random state = 2021 and fit the LR model\n",
    "\n",
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "logreg = LogisticRegression(random_state=2021)\n",
    "logreg.fit(X_train_scaled, y_train)\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bnmjHAupvpa4"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Plot Confusion Matrix using plot_confusion_matrix module :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "E1SXfdSUvpa4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e0f1d85c10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sV21gNWYHUjPLmQOpmVkZwjVSM7OytelAKum3FHjHNCLOrEiJzKzdiICoKTrdZ6tXqEY6pcAxM7Nm0aZrpBFxY/azpE0iYkXli2Rm7UZA1G74NdKik5ZI2kvSyyQTpSJpZ0m/r3jJzKxdiNrSttaslNmffgMcBCwGiIjngdEVLJOZtRsiorStNSup1z4i3q43QWpNY3nNzEoV7Wj409uS9gZC0kbAmaSP+WZm5apt4732dcYB/02ybsk7wMPAaZUslJm1E22ks6loII2IRcBxLVAWM2uH2kIgLaXXfmtJ90laKGmBpHskbd0ShTOzti1I20lL2IqRdEMao17MpN0maXq6zambPV/SUEmrMseuyZwzStIMSbMkXaUCK+jVKaXX/i/A7UA/oD8wAbilhPPMzApLH+1L2UowHhi71uUjjo6IkRExkmRhvDszh9+oOxYR4zLpVwMnk6zjNLz+NRtSSiBVRPwpIqrT7c+UsDypmVlxzTf8KSIeA5Y0eJekVnkURSqB6UqjPSPiqYgI4Cbg8GL3LvSufa9091FJPwFuJQmgRwP3F7uwmVkxAdSU3mtfdF37AvYF5kfEzEzaMEnPAUuBcyPicZJO9bmZPHPTtIIKdTZNJfmedd/ylMyxAC4qXnYzswKCpgy2L7iufRHHsnZttAoYHBGLJY0C7pa0PQ0voFf0CbzQu/bDmlpSM7OmqnSvvaROwFeBUWvuGfER8FG6P1XSG8AIkhrowMzpA4F5xe5R0ptNknYAtgM2zhTkplLONTMrpJQe+TIdALwaEWse2SX1BZZERE06Cmk48K+IWCJpmaQ9gaeB44HfFrtB0UAq6Txgf5JA+gBwMPAESSOsmdn6a8YB+ZJuIYlVfSTNBc6LiOuBY1i3k2k0cKGkapJX3sdFRF1H1akkIwC6Ag+mW0Gl1Ei/DuwMPBcR35a0JfDHEs4zMysoEDW1pQweKuFaEcc2kv5vDaTdQTIcqqH8U4AdmnLvUgLpqoiolVQtqSewAPCAfDNrFi3waF9xpQTSKZI2A64j6clfDjxTyUKZWfsQQG0rnyKvFKW8a/+9dPcaSQ+RDFZ9obLFMrN2oWnDn1qtQgPydy10LCKmVaZIZtaetPVH+8sLHAvgC81clmbzwoxZDNzmS3kXw5rg/VNG5l0Ea6Kev6tqluu06Uf7iPh8SxbEzNqf5uy1z1NJA/LNzCqlDTzZO5CaWX4i2vijvZlZS2gLvfalzJAvSd+U9F/p58GS9qh80cysPagtcWvNSmnl/T2wF8k0VADLgN9VrERm1o6IKHFrzUp5tP9sROyaToBKRLyXLstsZlaWAKrbwKN9KYF0taSOpJ1r6fRTrb2mbWYbiNZe2yxFKY/2VwF3AVtIuphkCr1LKloqM2sXgrbRRlrKu/Y3S5oKjCGZhv/wiHil4iUzs3ahLdRIS5nYeTCwErgvmxYRb1WyYGbWPrT22mYpSnm0vx/4a/pzEvAvSpgx2sysmABqUElbMZJukLRA0ouZtPMlvSNperodkjl2jqRZkl6TdFAmfZSkGemxq9KlnAsqGkgjYseI2Cn9ORzYg6Sd1MysbLUqbSvBeGBsA+lXRsTIdHsAQNJ2JEuQbJ+e8/u0Ux3gauBkknWchjdyzbU0ebaAdPq83Zt6nplZfYGoLXEreq2Ix4AlRTMmDgNujYiPImI2MAvYQ1I/kjmXn4qIIFmb7vBiFyuljfTfMx87ALsCC0ssrJlZQU2YtKSPpCmZz9dGxLUlnHe6pOOBKcDZEfEeMACYnMkzN01bne7XTy+olHGkPTL71SRtpQ0uGmVm1lRN6GxaFBG7NfHyVwMXkcTri0jmWT4RGqziRoH0ggoG0rTNoHtE/KjYhczMmiqA2uJ9Oet//Yj5dfuSriPpOIekpjkok3UgMC9NH9hAekGNtpFK6hQRNSSP8mZmFVFT4rY+0jbPOkcAdT369wLHSOoiaRhJp9IzEVEFLJO0Z9pbfzxwT7H7FKqRPkMSRKdLuheYAKyoOxgRdzblC5mZ1Rel98gXJekWYH+SttS5wHnA/pJGklR+5wCnAETES5JuB14mabI8La04ApxKMgKgK8lQz6LDPUtpI+0FLCZZo6muDSEAB1IzK1spPfKliIhjG0i+vkD+i4GLG0ifAuzQlHsXCqRbpD32L7JuI2xbWB3AzFqBthBMCgXSjkB31rMXy8ysmKSzKe9SlK9QIK2KiAtbrCRm1i61hXftCwXSNvDvhJm1ZgHUtIFIUyiQjmmxUphZu9Wma6QRUeo7q2Zm66VuYucNnZdjNrNctYElmxxIzSxfrpGamZUhmdh5w+dAama5auvjSM3MKsqdTWZmzcCB1MysTG3hfXMHUjPLTXNOo5cnB1Izy1Vb6LVv8iqiZmbNJelsipK2YhpZ1/4ySa9KekHSXZI2S9OHSlqVWe/+msw5zb+uvZlZJdWWuJVgPOuuQT8R2CEidgJeB87JHHsjs979uEx65de1NzNrTlHiVvQ6DaxrHxGPRER1+nEyay9st471XdfegdTMclM3jrSZaqTFnMja6y8Nk/ScpH9I2jdNG0CF1rU3M6uIAKpV8gCoPpKmZD5fGxHXlnKipP8kWeTu5jSpChgcEYsljQLulrQ9lVjX3sys0powjnRRROzW1OtLOgH4EjAmfVwnIj4CPkr3p0p6AxhBc69rb2bWEir5aC9pLPBj4CsRsTKT3ldSx3R/a5JOpX9VYl17M7OKihKHNpWikXXtzwG6ABPTUUyT0x760cCFkqpJhrKOy0xmX5F17c3MKqa5XhFtyrr2EXEHcEcjx5p1XXszs4rzpCVmZmVIJnbe8KctcSA1s1y5RmpmVqZwjdTMbP15hnyriJ49NuGyX5zJtiOGEAFn/+Q3nPTtw/jUsGSMcM+em7B06QoO+vIZdO7ciUt/fjo77zic2tpazrvoWp56ekbO36Dtm7v8Y0599C0WrKqmg+CET/dm3I59ee/Dak6c9CZvLfuYwT024v8dMITNunTi9pnv8dsXFqw5/6XFH/KPr45gxz5dmb5wJd/7+9t8WFPLgYN6cune/SlhsqE2pbmGP+WpRQKppN7ApPTjViTjthamn/eIiI9bohwbggv+62T+/thUTjn9F3Tu3ImuG3fhe2f+cs3xn53zHZYtS8YVf+PogwA44JDT6N17U/50w4Ucevj3SV/esArp1EH8fK/+7NynG8s+ruHzd73O/gN78JfXljB6QHd+MHJLrpw+nyunL+CCz/bnqOGbc9TwzQF4ackqjnt4Djv26QrA2U/M5TejB7L7Ft048qHZ/O3tZRw4uGeeX6/FtYX/W1vkzaaIWFw3XRVwDXBlZvqqjyW5Zgx0796Vz+6+A7fc/ggAq1dXs3TZirXyfPnQfbnnr/8AYPg2g3nyn88DsHjxByxdupyddxzesoVuh7bq1pmd+3QDoMdGHRmx2cZUrVjNg28u5dgRvQA4dkQvHpizdJ1z75j1Pl/71GYAvLtyNcs+rmWPLTdBEscM35z753zQYt+jNQigmihpa81ye0VU0nhJV0h6FPilpPMl/TBz/EVJQ9P9b0p6Jp2A9Q91r3a1NYMH9WPJkg+44lc/4KF7r+KyS86ka9cua45/dvftWbjofWbPSV79feXV2XzxgD3p2LEDgwZuyY47bEP/fn3yKn679Nayj3lh0SpGbdGNBatWs1W3zkASbBeuql4n/11vvM/XttkMgKoVq+nfvfOaY/036UzVytUtUu7WI0r+rzXL+137EcABEXF2YxkkfQY4GtgnrdHWAMc1kO9kSVMkTamNdf8H3hB06tSBHbbfhj/d/ABjv3ImK1d9yGnjjlxz/LAv78c99/1jzedbJzxC1buLeODu/+b8c09m6rRXqK5pC033G4blq2s4fuIcfrF3f3puVPzf9ikLVtC1Uwe265U81jfUAqMGJx9qu1p4Gr2KyfuRekJEFFuyZQwwCng2bYTvCiyonymdTutagM4du7buf74aUVW1mKp3F/Hc868BcP+DT64JpB07duDgg/bmkMPOWpO/pqaWCy6+bs3nuyf8mtlz3mnZQrdTq2uDEybO4chtNufLwzYDYIuunXl3ZVIrfXflavp2Xfuv152zPqmNAvTv3pl5yz+pgc5bsZqtuuX9V7LltfbaZinyrpFmGwCrWbs8G6c/BdyYaVPdNiLOb6kCtqSFi95jXtVCth6WzCP7ub13ZuastwDYd59deOONuVS9u3hN/o037rLm0X/ffUZSXV3DzFlvt3zB25mI4Ix/vM2IzTbmtJ36rkkfO6Qnt7yezHtxy+tLOHjIJ51GtRHcM/uDNe2jkDz+d9+oA8/OX0FEcOvM9zhk6KYt9j1aC9dIm9cckjkDkbQrMCxNnwTcI+nKiFggqRfQIyLezKeYlfWzC/7Ab6/8ERt17sSbb7/L2f/xGwC+8qXR3J15rAfo03tTbh5/EbW1wbvzF3PW2b/OocTtz+T5K7ht5nts12tj9r0jeXr42e79+MHILfj2397kz68uYWD3jRh/wJA15/yzagX9N+nM0J5d1rrW5Z8bmAx/qq7lgEE9OHBQjxb9LnkLoKYNjDJRSw+VkXQ+sJxkdpW/RsT/puldSeb92wJ4FvgccHBEzJF0NMl0WB2A1cBpETG5sXt07tg1enUfWsmvYc1s1reGFc9krUrP3z04dX0mWs7q3WXzOGir/UvKe8tbd5d9v0pp8RppY4/lEbEK+GIjx24DbqtgscwsBxtCj3wpWtOjvZm1Q629/bMUeXc2mVk7V5vOkl9sK0bSDZIWSHoxk9ZL0kRJM9Ofm2eOnSNplqTXJB2USR8laUZ67CqV8M6uA6mZ5SZZs77ZBuSPB8bWS/sJMCkihpN0XP8EQNJ2wDHA9uk5v8+86HM1cDLJOk7DG7jmOhxIzSw/kfTal7IVvVTEY8CSesmHATem+zcCh2fSb42IjyJiNjAL2ENSP6BnRDyVrjh6U+acRrmN1Mxyk7zZVNF17bdMVwYlIqokbZGmDwCyI3/mpmmr0/366QU5kJpZrprQ2bRe69o3oqF2zyiQXpAf7c0sRxWftGR++rhO+rPu9fK5wKBMvoHAvDR9YAPpBTmQmllu6h7tm6PXvhH3Aiek+yeQvPRTl36MpC6ShpF0Kj2TNgMsk7Rn2lt/fOacRvnR3sxy1VxvV0q6BdifpC11LnAecClwu6TvAG8BR6b3fEnS7cDLJPN8nJaZQOlUkhEAXYEH060gB1Izy01zLsccEcc2cmhMI/kvBi5uIH0KySvsJXMgNbNcec0mM7OyRJtYY8yB1Mxy08RxpK2WA6mZ5cqzP5mZlaktTOzsQGpmufGjvZlZ2coabN9qOJCaWa7ca29mVgY/2puZNQP32puZlSEiqIkNf9UmB1Izy5XbSM3MyuQ2UjOzMrmN1MysDAHU+tHezKwcZS0j0mp4qREzy00ANVFb0laIpG0lTc9sSyV9X9L5kt7JpB+SOeccSbMkvSbpoHK+h2ukZpar5ni0j4jXgJEAkjoC7wB3Ad8GroyIX2fzS9oOOAbYHugP/E3SiMxyI03iGqmZ5aoCq4iOAd6IiDcL5DkMuDUiPoqI2cAsYI/1/Q4OpGaWm4igtsSNZFG7KZnt5EYuewxwS+bz6ZJekHSDpM3TtAHA25k8c9O09eJAama5akKNdFFE7JbZrq1/LUkbAV8BJqRJVwOfInnsrwIur8vaYFHWk9tIzSxXNevXLNmYg4FpETEfoO4ngKTrgL+mH+cCgzLnDQTmre9NXSM1s9xEuvhdKVuJjiXzWC+pX+bYEcCL6f69wDGSukgaBgwHnlnf7+EaqZnlqrleEZXUDTgQOCWT/CtJI0ke2+fUHYuIlyTdDrwMVAOnrW+PPTiQmlnOmmvSkohYCfSul/atAvkvBi5ujns7kJpZbvyKqJlZM2gLr4g6kJpZfjyxs5lZeQJP7GxmVja3kZqZlck1UjOzMgThpUbMzMrlGqmZWTkC99qbmZXDA/LNzMrWpAlJWi0HUjPLld9sMjMrk2ukZmZlaCttpGoL/xrUJ2khUGjhqw1ZH2BR3oWwJmmrv7MhEdG3nAtIeojkz6cUiyJibDn3q5Q2GUjbMklTImK3vMthpfPvrO3zUiNmZmVyIDUzK5MD6YZnnSVordXz76yNcxupmVmZXCM1MyuTA6mZWZk8ID9nkmqAGZmkwyNiTiN5l0dE9xYpmBUkqTcwKf24FVADLEw/7xERH+dSMMuF20hz1pTg6EDaOkk6H1geEb/OpHWKiOr8SmUtyY/2rYyk7pImSZomaYakwxrI00/SY5KmS3pR0r5p+hclPZWeO0GSg24LkjRe0hWSHgV+Kel8ST/MHH9R0tB0/5uSnkl/h3+Q1DGvclv5HEjz1zX9yzRd0l3Ah8AREbEr8Hngckmqd843gIcjYiSwMzBdUh/gXOCA9NwpwL+32LewOiNIfgdnN5ZB0meAo4F90t9hDXBcyxTPKsFtpPlblf5lAkBSZ+ASSaOBWmAAsCXwbuacZ4Eb0rx3R8R0SfsB2wFPpnF3I+CplvkKljEhImqK5BkDjAKeTX9XXYEFlS6YVY4DaetzHNAXGBURqyXNATbOZoiIx9JAeyjwJ0mXAe8BEyPi2JYusK1lRWa/mrWf+up+jwJujIhzWqxUVlF+tG99NgUWpEH088CQ+hkkDUnzXAdcD+wKTAb2kbRNmqebpBEtWG5b1xyS3w2SdgWGpemTgK9L2iI91iv9ndoGyjXS1udm4D5JU4DpwKsN5Nkf+JGk1cBy4PiIWCjp34BbJHVJ850LvF7xEltj7gCOlzSdpDnmdYCIeFnSucAjkjoAq4HTaLtTP7Z5Hv5kZlYmP9qbmZXJgdTMrEwOpGZmZXIgNTMrkwOpmVmZHEjbKUk1mXf1J0jqVsa1xkv6err/R0nbFci7v6S91+Mec9LXYEtKr5dneRPvtdY78mbFOJC2X6siYmRE7AB8DIzLHlzfSTQi4qSIeLlAlv2BJgdSs9bMgdQAHge2SWuLj0r6CzBDUkdJl0l6VtILkk4BUOJ/JL0s6X5gi7oLSfq7pN3S/bHpTFTPpzNaDSUJ2D9Ia8P7Suor6Y70Hs9K2ic9t7ekRyQ9J+kPJK9VFiTpbklTJb0k6eR6xy5PyzJJUt807VOSHkrPeVzSp5vlT9PaHb/Z1M5J6gQcDDyUJu0B7BARs9Ng9EFE7J6+LfWkpEeAXYBtgR1JJlR5Gbih3nX7AtcBo9Nr9YqIJZKuITN3Zxq0r4yIJyQNBh4GPgOcBzwRERdKOhRYKzA24sT0Hl1JJgS5IyIWA5sA0yLibEn/lV77dJJF6cZFxExJnwV+D3xhPf4YrZ1zIG2/uqavLkJSI72e5JH7mYiYnaZ/Ediprv2TZB6A4cBo4JZ0lqN5kv6vgevvCTxWd62IWNJIOQ4AtsvMFNhTUo/0Hl9Nz71f0nslfKczJR2R7g9Ky7qYZBat29L0PwN3KpmrdW9gQubeXTBbDw6k7dda0/cBpAElO3uRgDMi4uF6+Q4Bir1brBLyQNK8tFdErGqgLCW/vyxpf5KgvFdErJT0d+rNmpUR6X3fr/9nYLY+3EZqhTwMnJrOe4qkEZI2AR4DjknbUPuRTEBd31PAfpKGpef2StOXAT0y+R4hecwmzTcy3X2MdLJjSQcDmxcp66bAe2kQ/TRJjbhOB6CuVv0NkiaDpcBsSUem95CknYvcw6xBDqRWyB9J2j+nSXoR+APJU8xdwEySRfuuBv5R/8SIWEjSrnmnpOf55NH6PuCIus4m4Exgt7Qz62U+GT1wATBa0jSSJoa3ipT1IaCTpBeAi0imFayzAthe0lSSNtAL0/TjgO+k5XsJWGdZF7NSePYnM7MyuUZqZlYmB1IzszI5kJqZlcmB1MysTA6kZmZlciA1MyuTA6mZWZn+P//SiIqo9/zKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Use display labels as per your choice.\n",
    "\n",
    "plot_confusion_matrix(logreg,X_test_scaled,y_test, \n",
    "                     values_format='d',\n",
    "                     display_labels=[\"False\",\"True\"],cmap='rocket')\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pgN165X7vpa4"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print out Classification Report for LR :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "FzcnzDD9vpa4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.83      0.82      3300\n",
      "           1       0.79      0.75      0.77      2749\n",
      "\n",
      "    accuracy                           0.80      6049\n",
      "   macro avg       0.80      0.79      0.79      6049\n",
      "weighted avg       0.80      0.80      0.80      6049\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "y_pred_logreg=logreg.predict(X_test_scaled)\n",
    "print(classification_report(y_test,y_pred_logreg))\n",
    "\n",
    "## ================================ ##\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xle8Hr2vvpa4"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print Training and Testing Score along with Accuracy score for LR :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ND1kWpJMvpa5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Score: 0.7989199316691464\n",
      "Test Data Score: 0.7969912382211936\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "print(\"Train Data Score: {}\".format(logreg.score(X_train_scaled, y_train)))\n",
    "print(\"Test Data Score: {}\".format(logreg.score(X_test_scaled, y_test)))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "jm3sW4ZTvpa5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy ->  0.7969912382211936\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Print Accuracy Score for LR\n",
    "\n",
    "print('Accuracy -> ',accuracy_score(y_test,y_pred_logreg))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T16gbfRQvpa5"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print ROC - AUC Score for LR :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "IV0ubSL9vpa5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Scores: 0.7933187274711465\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Right way to calculate ROC_AUC score is by using predict_proba method given in official Docs. Sorry from our side.\n",
    "\n",
    "print('ROC AUC Scores: {}'.format(roc_auc_score(y_test, y_pred_logreg)))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "og34rzbSvpa5"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Train SVM with C = 50 and gamma = scale :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "_fw1j2cxvpa5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Set probability = True \n",
    "\n",
    "clf_svm = SVC(random_state=2021,C=50,gamma='scale',kernel='rbf',probability=True)\n",
    "clf_svm.fit(X_train_scaled, y_train)\n",
    "\n",
    "%time\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y8VRDRHXvpa6"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Plot Confusion Matrix for SVM :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "1TTVlekPvpa6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e0f8691340>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Use display labels as per your choice.\n",
    "\n",
    "plot_confusion_matrix(clf_svm,X_test_scaled,y_test, \n",
    "                     values_format='d',\n",
    "                     display_labels=[\"False\",\"True\"],cmap='rocket')\n",
    "\n",
    "\n",
    "## ================================ ##\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TNWvmLSivpa6"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print out Classification Report for SVM :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "yQDf6wOdvpa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.81      0.80      3300\n",
      "           1       0.76      0.76      0.76      2749\n",
      "\n",
      "    accuracy                           0.78      6049\n",
      "   macro avg       0.78      0.78      0.78      6049\n",
      "weighted avg       0.78      0.78      0.78      6049\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "y_pred1=clf_svm.predict(X_test_scaled)\n",
    "print(classification_report(y_test,y_pred1))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SfF49qwIvpa7"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print out Accuracy score for SVM :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "lH1lkfsFvpa7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy ->  0.7834352785584394\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "print('Accuracy -> ',accuracy_score(y_test,y_pred1))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0tlgy05Jvpa7"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print Training and Testing Score for SVM :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "yqofS78Xvpa7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Score: 0.9291342921695046\n",
      "Test Data Score: 0.7834352785584394\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "print(\"Train Data Score: {}\".format(clf_svm.score(X_train_scaled, y_train)))\n",
    "print(\"Test Data Score: {}\".format(clf_svm.score(X_test_scaled, y_test)))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ti_PCgHpvpa7"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Plot ROC curve and calculate ROC - AUC score for SVM :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "iml9q7hrvpa8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Set appropriate X label, Y label and Title for the graph\n",
    "\n",
    "y_pred_svm_proba = clf_svm.predict_proba(X_test_scaled)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_svm_proba[:,1])\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(fpr,tpr,'-r',linewidth=1)\n",
    "plt.plot([0,1], [0,1], 'k--' )\n",
    "plt.title('ROC curve for SVM (C=50)')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "T_ninnLwvpa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Scores: 0.7811374384073547\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Print out ROC score for SVM\n",
    "\n",
    "print('ROC AUC Scores: {}'.format(roc_auc_score(y_test, y_pred1)))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YmpeyKWWvpa8"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Train SVM with C = 1 and gamma = scale :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "z9mfbGb9vpa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1, probability=True, random_state=2021)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Train SVM with new mentioned parameters\n",
    "\n",
    "%time\n",
    "clf_svm_new = SVC(random_state=2021,probability=True,C=1,gamma='scale')\n",
    "clf_svm_new.fit(X_train_scaled, y_train)\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pKOp4Cbyvpa9"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print out Classification Report for new SVM :</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "dPLlDBz0vpa9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.84      0.83      3300\n",
      "           1       0.80      0.78      0.79      2749\n",
      "\n",
      "    accuracy                           0.81      6049\n",
      "   macro avg       0.81      0.81      0.81      6049\n",
      "weighted avg       0.81      0.81      0.81      6049\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "plot_confusion_matrix(clf_svm_new,X_test_scaled,y_test, \n",
    "                     values_format='d',\n",
    "                     display_labels=[\"False\",\"True\"],cmap='rocket')\n",
    "\n",
    "y_pred_svm=clf_svm_new.predict(X_test_scaled)\n",
    "print(classification_report(y_test,y_pred_svm))\n",
    "\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c71VaRy9vpa9"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Plot ROC curve and calculate ROC - AUC score for the new SVM :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "iT8NNVKkvpa9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Scores: 0.8072020128531587\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "y_pred_svm_proba_new = clf_svm_new.predict_proba(X_test_scaled)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_svm_proba_new[:,1])\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(fpr,tpr,'-r',linewidth=1)\n",
    "plt.plot([0,1], [0,1], 'k--' )\n",
    "plt.title('ROC curve for SVM (C=1)')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "print('ROC AUC Scores: {}'.format(roc_auc_score(y_test, y_pred_svm)))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6tlhrJ8qvpa-"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Print Training and Testing Score along with accuracy score for the new SVM :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "AnydqRTQvpa-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Score: 0.8343527855843941\n",
      "Test Data Score: 0.8098859315589354\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "print(\"Train Data Score: {}\".format(clf_svm_new.score(X_train_scaled, y_train)))\n",
    "print(\"Test Data Score: {}\".format(clf_svm_new.score(X_test_scaled, y_test)))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "epvTM-Tcvpa-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy ->  0.8098859315589354\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Print out Accuracy score for the new SVM\n",
    "\n",
    "print('Accuracy -> ',accuracy_score(y_test,y_pred_svm))\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wwVQ1LTKvpa-"
   },
   "source": [
    "<h1 style=\"background-color:#ABFF9B;font-size:20px;color:#00033E;font-weight : bold\">Calculate Average Cross-Val score for the new SVM with 5 folds  :</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "jC0bK5Fsvpa-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:[0.81432507 0.80523416 0.81620281 0.81344723 0.80986498]\n",
      "Average cross-validation score: 0.8118148493122817\n"
     ]
    }
   ],
   "source": [
    "## ======== YOUR CODE HERE ======== ##\n",
    "\n",
    "## Find mean for the calculated CV scores\n",
    "\n",
    "scores = cross_val_score(clf_svm_new, X_train_scaled, y_train, cv = 5)\n",
    "print('Cross-validation scores:{}'.format(scores))\n",
    "\n",
    "print('Average cross-validation score: {}'.format(scores.mean()))\n",
    "\n",
    "\n",
    "## ================================ ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QxWC2wNLvpa-"
   },
   "source": [
    "<br>\n",
    "<h1 style = \"font-size:30px; font-family:Brush Script MT ; font-weight : bold; color : #0331A8; text-align: center; border-radius: 10px 10px;\">The End</h1>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_s1d-tV4vpa_"
   },
   "outputs": [],
   "source": []
  }
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