Modelling Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem statement is to predict the Surge Price Type \n",
    " This is a multi-Label classification problem statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src =\"../img/2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/train.csv\")\n",
    "test = pd.read_csv(\"data/test.csv\")\n",
    "sample = pd.read_csv(\"data/sample_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((131662, 14), (87395, 13))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip_ID</th>\n",
       "      <th>Trip_Distance</th>\n",
       "      <th>Type_of_Cab</th>\n",
       "      <th>Customer_Since_Months</th>\n",
       "      <th>Life_Style_Index</th>\n",
       "      <th>Confidence_Life_Style_Index</th>\n",
       "      <th>Destination_Type</th>\n",
       "      <th>Customer_Rating</th>\n",
       "      <th>Cancellation_Last_1Month</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Surge_Pricing_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>T0005689460</td>\n",
       "      <td>6.77</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.42769</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>3.90500</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>46</td>\n",
       "      <td>60</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>T0005689461</td>\n",
       "      <td>29.47</td>\n",
       "      <td>B</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.78245</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>3.45000</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>56</td>\n",
       "      <td>78</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>T0005689464</td>\n",
       "      <td>41.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>3.50125</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>77</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>T0005689465</td>\n",
       "      <td>61.56</td>\n",
       "      <td>C</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>3.45375</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>74</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>T0005689467</td>\n",
       "      <td>54.95</td>\n",
       "      <td>C</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.03453</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>3.40250</td>\n",
       "      <td>4</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49</td>\n",
       "      <td>102</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_ID  Trip_Distance Type_of_Cab  Customer_Since_Months  \\\n",
       "0  T0005689460           6.77           B                    1.0   \n",
       "1  T0005689461          29.47           B                   10.0   \n",
       "2  T0005689464          41.58         NaN                   10.0   \n",
       "3  T0005689465          61.56           C                   10.0   \n",
       "4  T0005689467          54.95           C                   10.0   \n",
       "\n",
       "   Life_Style_Index Confidence_Life_Style_Index Destination_Type  \\\n",
       "0           2.42769                           A                A   \n",
       "1           2.78245                           B                A   \n",
       "2               NaN                         NaN                E   \n",
       "3               NaN                         NaN                A   \n",
       "4           3.03453                           B                A   \n",
       "\n",
       "   Customer_Rating  Cancellation_Last_1Month  Var1  Var2  Var3  Gender  \\\n",
       "0          3.90500                         0  40.0    46    60  Female   \n",
       "1          3.45000                         0  38.0    56    78    Male   \n",
       "2          3.50125                         2   NaN    56    77    Male   \n",
       "3          3.45375                         0   NaN    52    74    Male   \n",
       "4          3.40250                         4  51.0    49   102    Male   \n",
       "\n",
       "   Surge_Pricing_Type  \n",
       "0                   2  \n",
       "1                   2  \n",
       "2                   2  \n",
       "3                   3  \n",
       "4                   2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip_ID</th>\n",
       "      <th>Trip_Distance</th>\n",
       "      <th>Type_of_Cab</th>\n",
       "      <th>Customer_Since_Months</th>\n",
       "      <th>Life_Style_Index</th>\n",
       "      <th>Confidence_Life_Style_Index</th>\n",
       "      <th>Destination_Type</th>\n",
       "      <th>Customer_Rating</th>\n",
       "      <th>Cancellation_Last_1Month</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>T0005689459</td>\n",
       "      <td>9.44</td>\n",
       "      <td>A</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.57438</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>3.68000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>T0005689462</td>\n",
       "      <td>32.15</td>\n",
       "      <td>B</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.85143</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>1.59375</td>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>49</td>\n",
       "      <td>80</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>T0005689463</td>\n",
       "      <td>10.38</td>\n",
       "      <td>C</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.70530</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>4.50500</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>74</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>T0005689466</td>\n",
       "      <td>14.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.48159</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>4.53000</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>43</td>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>T0005689468</td>\n",
       "      <td>32.03</td>\n",
       "      <td>B</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.81598</td>\n",
       "      <td>A</td>\n",
       "      <td>K</td>\n",
       "      <td>4.60125</td>\n",
       "      <td>3</td>\n",
       "      <td>96.0</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_ID  Trip_Distance Type_of_Cab  Customer_Since_Months  \\\n",
       "0  T0005689459           9.44           A                   10.0   \n",
       "1  T0005689462          32.15           B                   10.0   \n",
       "2  T0005689463          10.38           C                    4.0   \n",
       "3  T0005689466          14.94         NaN                    6.0   \n",
       "4  T0005689468          32.03           B                    7.0   \n",
       "\n",
       "   Life_Style_Index Confidence_Life_Style_Index Destination_Type  \\\n",
       "0           2.57438                           A                B   \n",
       "1           2.85143                           A                A   \n",
       "2           2.70530                           A                D   \n",
       "3           2.48159                           C                E   \n",
       "4           2.81598                           A                K   \n",
       "\n",
       "   Customer_Rating  Cancellation_Last_1Month  Var1  Var2  Var3  Gender  \n",
       "0          3.68000                         2   NaN    46    63    Male  \n",
       "1          1.59375                         0  65.0    49    80  Female  \n",
       "2          4.50500                         0   NaN    47    74    Male  \n",
       "3          4.53000                         0  63.0    43    54    Male  \n",
       "4          4.60125                         3  96.0    44    56    Male  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trip_ID                            0\n",
       "Trip_Distance                      0\n",
       "Type_of_Cab                    20210\n",
       "Customer_Since_Months           5920\n",
       "Life_Style_Index               20193\n",
       "Confidence_Life_Style_Index    20193\n",
       "Destination_Type                   0\n",
       "Customer_Rating                    0\n",
       "Cancellation_Last_1Month           0\n",
       "Var1                           71030\n",
       "Var2                               0\n",
       "Var3                               0\n",
       "Gender                             0\n",
       "Surge_Pricing_Type                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    56728\n",
       "3    47720\n",
       "1    27214\n",
       "Name: Surge_Pricing_Type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Surge_Pricing_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.metrics import log_loss,accuracy_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier,RandomForestClassifier,GradientBoostingClassifier,\\\n",
    "                                BaggingClassifier,ExtraTreesClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import catboost as cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_valid(model,X,y,cv=3):\n",
    "    results = cross_val_score(model, X,y,cv=cv,scoring='accuracy')\n",
    "    return results.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cancellation_Last_1Month',\n",
       " 'Trip_Distance',\n",
       " 'Var2',\n",
       " 'Var3',\n",
       " 'Life_Style_Index',\n",
       " 'Confidence_Life_Style_Index',\n",
       " 'Customer_Rating',\n",
       " 'Customer_Since_Months',\n",
       " 'Type_of_Cab',\n",
       " 'Destination_Type',\n",
       " 'Gender',\n",
       " 'Var1']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(set(train.columns)-set(['Trip_ID', 'Surge_Pricing_Type']))\n",
    "target = 'Surge_Pricing_Type'\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.copy()\n",
    "X_test = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Type_of_Cab','Confidence_Life_Style_Index']:\n",
    "    X_train[i] = X_train[i].astype(str)\n",
    "    X_test[i] = X_test[i].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train & test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, validX, trainY, validY = train_test_split(X_train[features], \n",
    "                                                  X_train[target], test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model= cat.CatBoostClassifier(iterations=1000,\n",
    "                              verbose=0,\n",
    "                              random_state=10000,\n",
    "                              early_stopping_rounds=50,\n",
    "                              cat_features=['Type_of_Cab','Confidence_Life_Style_Index','Destination_Type','Gender']\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=OneVsRestClassifier(estimator=OneVsRestClassifier(estimator=<catboost.core.CatBoostClassifier object at 0x7fb1ac050750>,\n",
       "                                                                                n_jobs=None),\n",
       "                                                  n_jobs=None),\n",
       "                    n_jobs=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "model = OneVsRestClassifier(model)\n",
    "\n",
    "model.fit(X=trainX,\n",
    "         y=trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.699692401169635"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid = model.predict(validX)\n",
    "accuracy_score(validY,y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = model.predict(X_test[features])\n",
    "test['Surge_Pricing_Type']  = y_test\n",
    "test[['Trip_ID','Surge_Pricing_Type']].to_csv(\"sampleSubmission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: This model gave 0.7054 public score but 0.7064 private score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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