Classification - IPL Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the libraries\n",
    "import pandas as pd                   # for data manipulation\n",
    "import seaborn as sns                 # for data visualisation\n",
    "import matplotlib.pyplot as plt       # for data visualisation\n",
    "from sklearn.model_selection import train_test_split           # for splitting the dataset\n",
    "from sklearn.preprocessing import StandardScaler               # for scaling the dataset\n",
    "from sklearn.linear_model import LogisticRegression            # Logistic Regression model\n",
    "from sklearn.metrics import confusion_matrix                   # accuracy measure for the model\n",
    "from sklearn.metrics import balanced_accuracy_score            # accuracy measure for the model\n",
    "import numpy as np                                             # for mathematical operations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the classification model using logistic regression, the target variable is \"Retained\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Is Captain(1=yes)</th>\n",
       "      <th>Is Wktkeeper(1=Yes)</th>\n",
       "      <th>Nationality(1=Overseas)</th>\n",
       "      <th>Team</th>\n",
       "      <th>IPL 4 Franchise</th>\n",
       "      <th>Player cost USD</th>\n",
       "      <th>matches</th>\n",
       "      <th>innings played</th>\n",
       "      <th>no</th>\n",
       "      <th>runs scored</th>\n",
       "      <th>...</th>\n",
       "      <th>half_centuries_ipl</th>\n",
       "      <th>fours_ipl</th>\n",
       "      <th>sixes_ipl</th>\n",
       "      <th>matches_bowl_ipl</th>\n",
       "      <th>balls_bowled_ipl</th>\n",
       "      <th>total_runs_given_ipl</th>\n",
       "      <th>wickets_taken_ipl</th>\n",
       "      <th>bowling_avg_ipl</th>\n",
       "      <th>economy_ipl</th>\n",
       "      <th>3wickets_ipl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>817.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Is Captain(1=yes)  Is Wktkeeper(1=Yes)  Nationality(1=Overseas)  \\\n",
       "0                0.0                  0.0                        1   \n",
       "1                0.0                  0.0                        1   \n",
       "2                0.0                  0.0                        1   \n",
       "3                0.0                  0.0                        1   \n",
       "4                0.0                  0.0                        1   \n",
       "\n",
       "           Team              IPL 4 Franchise  Player cost USD  matches  \\\n",
       "0     Australia               Mumbai Indians          20000.0     40.0   \n",
       "1  South Africa  Royal Challengers Bangalore          20000.0     18.0   \n",
       "2     Australia              Kings XI Punjab          20000.0     23.0   \n",
       "3  South Africa  Royal Challengers Bangalore          20000.0     20.0   \n",
       "4  South Africa             Delhi Daredevils          20000.0     25.0   \n",
       "\n",
       "   innings played   no  runs scored  ...  half_centuries_ipl  fours_ipl  \\\n",
       "0            40.0  3.0        817.0  ...                 0.0        0.0   \n",
       "1            18.0  1.0        356.0  ...                 0.0        0.0   \n",
       "2             8.0  5.0         33.0  ...                 0.0        0.0   \n",
       "3            20.0  0.0        343.0  ...                 0.0        0.0   \n",
       "4            22.0  8.0        213.0  ...                 0.0        0.0   \n",
       "\n",
       "   sixes_ipl  matches_bowl_ipl  balls_bowled_ipl  total_runs_given_ipl  \\\n",
       "0        0.0               0.0               0.0                   0.0   \n",
       "1        0.0               0.0               0.0                   0.0   \n",
       "2        0.0               0.0               0.0                   0.0   \n",
       "3        0.0               0.0               0.0                   0.0   \n",
       "4        0.0               0.0               0.0                   0.0   \n",
       "\n",
       "   wickets_taken_ipl  bowling_avg_ipl  economy_ipl  3wickets_ipl  \n",
       "0                0.0              0.0          0.0           0.0  \n",
       "1                0.0              0.0          0.0           0.0  \n",
       "2                0.0              0.0          0.0           0.0  \n",
       "3                0.0              0.0          0.0           0.0  \n",
       "4                0.0              0.0          0.0           0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the cleaned dataset\n",
    "df = pd.read_csv(\"Clean_Data.csv\")\n",
    "\n",
    "# displaying the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Distribution of the categories of the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankit\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Retained', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kgackuQlYAWwDtiQ5DDgV+NukXUz2kP08/+QuBocDhzG4bMh8Ptbdbuv2BfBzwPPmnC94BrASeClwRVXtBe5J8plH/VNJj5JRkAa+V1UvSPIM4BMMzilcBny7ql4wxPMvA9ZU1c1J3gSctp/tHuhu9/LQ/38Bzqmqh4WkO5rwOjQaK88pSHNU1XeAtwPvAr4H3JnkTGjv8T+/2/S7wNPmPPVpwK4kTwTe8Ch3+yngV7rnkuTHkzwVuA5Y251zWA787EJ/LmlYRkHaR1XdCNzM4DLibwDWJ7kZuJWHflXplcC7k9yY5ETgt4HrgS3AVx/lLi9hcL7gS90vqP9zBkcRHwd2AF8B/hT47A/zc0nD8CqpkqTGIwVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSY1RkCQ1/w+Cx5UoXdVkZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Retained'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    117\n",
       "1     12\n",
       "Name: Retained, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Retained'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9069767441860465"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "117/(117+12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two classes are distributed in the ratio of 91:9 in the target variable, hence  this is a case of imbalanced classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding the categorical variables with multiple categories\n",
    "data = pd.get_dummies(df, columns=['Team', 'IPL 4 Franchise'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129, 68)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the target and predictor dataframes\n",
    "X = data.drop('Retained', axis=1)\n",
    "y = data['Retained']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(129, 67)\n",
      "(129,)\n"
     ]
    }
   ],
   "source": [
    "# printing the shapes of the target and predictor dataframes\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling and Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the predictor variables\n",
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting into train and test datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    81\n",
       "1     9\n",
       "Name: Retained, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "81/(81+9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising the model \n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding predictions for the test set\n",
    "y_pred_lr = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6388888888888888"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34,  2],\n",
       "       [ 2,  1]], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# in training set\n",
    "\n",
    "Class 0 : 90%<br>\n",
    "Class 1 : 10%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the default logistic regression model, the balanced accuracy score comes out as 64% and there are 4 misclassifications. \n",
    "\n",
    "Hence, as an improvement over this model, the class weights are specified in the next model. A best practice is to use the inverse of the actual class distributions as the weights to the logistic regression model. Hence, for class 0, the weight will be 10 and 90 for class 1 of the target variable. This ensures that when there is a misclassification of the minority class, here, class 1, the model is penalised more than in the case of a misclassification of the majority class or class 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising the model \n",
    "model1 = LogisticRegression(class_weight={0:10, 1:90})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight={0: 10, 1: 90})"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "model1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding predictions for the test set\n",
    "y_pred_lr = model1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9722222222222222"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34,  2],\n",
       "       [ 0,  3]], dtype=int64)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model accuracy improves greatly to 97% and the misclassifications are reduced by half."
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
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  "language_info": {
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