Classification of data through random forest Kaggle Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from imblearn.over_sampling import RandomOverSampler, BorderlineSMOTE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('iith_foml_2020_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastcolumn_np = df.iloc[:,-1:].to_numpy()\n",
    "pranay=np.ravel(lastcolumn_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop(['Target Variable (Discrete)','Feature 16','Feature 17'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Ana\\lib\\site-packages\\sklearn\\impute\\_iterative.py:669: ConvergenceWarning: [IterativeImputer] Early stopping criterion not reached.\n",
      "  warnings.warn(\"[IterativeImputer] Early stopping criterion not\"\n"
     ]
    }
   ],
   "source": [
    "imp_mean = IterativeImputer(random_state=0)\n",
    "imp_mean.fit(df_copy)\n",
    "IterativeImputer(random_state=0)\n",
    "data = imp_mean.transform(df_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{10, 20, 12, 13}\n"
     ]
    }
   ],
   "source": [
    "correlated_features = set()\n",
    "correlation_matrix = pd.DataFrame(data).corr()\n",
    "for i in range(len(correlation_matrix.columns)):\n",
    "    for j in range(i):\n",
    "        if abs(correlation_matrix.iloc[i, j]) > 0.8:\n",
    "            colname = correlation_matrix.columns[i]\n",
    "            correlated_features.add(colname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_drop = np.delete(data, [10,20,12,13],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dict = {17:10, 12:10, 11:10, 16:10, 10:10, 9:10}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "over = RandomOverSampler(random_state=0, sampling_strategy=Dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_resampled, y_resampled = over.fit_resample(np_drop, pranay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = BorderlineSMOTE(random_state=0, k_neighbors=2, kind='borderline-2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_resampled, y_resampled = sm.fit_resample(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 2000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 70,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_estimators = [2000]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [50,60,70]\n",
    "min_samples_split = [2, 5]\n",
    "min_samples_leaf = [1, 2]\n",
    "bootstrap = [True, False]# Create the random grid\n",
    "params = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "cv_result = RandomizedSearchCV(RandomForestClassifier(),params,cv=3,scoring='accuracy', random_state=42)\n",
    "cv_result.fit(X_resampled, y_resampled)\n",
    "cv_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, max_depth=70, n_estimators=2000,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_7 = RandomForestClassifier(n_estimators = 2000, min_samples_split=2, min_samples_leaf=1, max_features='auto',\n",
    "                               max_depth=70, random_state = 42, bootstrap=False)\n",
    "data_7.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2=pd.read_csv('iith_foml_2020_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_copy = df_2.copy()\n",
    "df_2_copy.drop(['Feature 16','Feature 17'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2 = imp_mean.transform(df_2_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_drop_2 = np.delete(data_2, [10,20,12,13],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pred = data_7.predict(np_drop_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_id = np.linspace(1,426,426,dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_df = pd.DataFrame(columns=['Id','Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_df['Id'] = col_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_df['Category'] = data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_df.to_csv('test_ouput.csv', index=False)"
   ]
  }
 ],
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