Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,scale,MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression,LinearRegression,Lasso\n",
    "from sklearn.metrics import roc_curve,roc_auc_score\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "df=pd.read_csv('train.csv')\n",
    "df1=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Cabin.replace(np.nan,'NC',inplace=True)\n",
    "df1.Cabin.replace(np.nan,'NC',inplace=True)\n",
    "df.Embarked.replace(np.nan,'N',inplace=True)\n",
    "df1.Embarked.replace(np.nan,'N',inplace=True)\n",
    "df['Embarked']=df['Embarked'].replace(['S','C','Q','N'],[0,1,2,3])\n",
    "df1['Embarked']=df1['Embarked'].replace(['S','C','Q','N'],[0,1,2,3])\n",
    "df['Sex']=df['Sex'].replace(['male','female'],[0,1])\n",
    "df1['Sex']=df1['Sex'].replace(['male','female'],[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.4542\n",
      "14.4542\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null int64\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           418 non-null float64\n",
      "Cabin          418 non-null object\n",
      "Embarked       418 non-null int64\n",
      "gp_age         418 non-null float64\n",
      "f_cnt          418 non-null int64\n",
      "dtypes: float64(3), int64(7), object(3)\n",
      "memory usage: 42.5+ KB\n"
     ]
    }
   ],
   "source": [
    "median=np.median(df['Fare'])\n",
    "print(median)\n",
    "df.Fare.replace(0,median,inplace=True)\n",
    "median=np.median(df1['Fare'])\n",
    "print(median)\n",
    "df1.Fare.replace(np.nan,median,inplace=True)\n",
    "df1.Fare.replace(0,median,inplace=True)\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n",
      "30.272590361445783\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null int64\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           418 non-null float64\n",
      "Cabin          418 non-null object\n",
      "Embarked       418 non-null int64\n",
      "gp_age         418 non-null float64\n",
      "f_cnt          418 non-null int64\n",
      "dtypes: float64(3), int64(7), object(3)\n",
      "memory usage: 42.5+ KB\n"
     ]
    }
   ],
   "source": [
    "mean=np.mean(df['Age'])\n",
    "df['gp_age']=df['Age']\n",
    "df1['gp_age']=df1['Age']\n",
    "print(mean)\n",
    "for x in range(891):\n",
    "    if math.isnan(df.loc[x,'Age']):\n",
    "        df.loc[x,'Age']=mean\n",
    "    if df.loc[x,'Age'] < 16:\n",
    "        df.loc[x,'gp_age']=0\n",
    "    elif df.loc[x,'Age'] < 32:\n",
    "        df.loc[x,'gp_age']=1\n",
    "    elif df.loc[x,'Age'] < 48:\n",
    "        df.loc[x,'gp_age']=2\n",
    "    elif df.loc[x,'Age'] < 64:\n",
    "        df.loc[x,'gp_age']=3\n",
    "    elif df.loc[x,'Age'] <= 80:\n",
    "        df.loc[x,'gp_age']=4\n",
    "mean=np.mean(df1['Age'])\n",
    "print(mean)\n",
    "for x in range(418):\n",
    "    if math.isnan(df1.loc[x,'Age']):\n",
    "        df1.loc[x,'Age']=mean\n",
    "    if df1.loc[x,'Age'] < 16:\n",
    "        df1.loc[x,'gp_age']=0\n",
    "    elif df1.loc[x,'Age'] < 32:\n",
    "        df1.loc[x,'gp_age']=1\n",
    "    elif df1.loc[x,'Age'] < 48:\n",
    "        df1.loc[x,'gp_age']=2\n",
    "    elif df1.loc[x,'Age'] < 64:\n",
    "        df1.loc[x,'gp_age']=3\n",
    "    elif df1.loc[x,'Age'] <= 80:\n",
    "        df1.loc[x,'gp_age']=4\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['f_cnt']=df['SibSp']+df['Parch']\n",
    "df1['f_cnt']=df1['SibSp']+df1['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'gp_age', 'f_cnt']\n"
     ]
    }
   ],
   "source": [
    "#df.Survived=df.Survived.astype('category')\n",
    "df.Sex=df.Sex.astype('category')\n",
    "df.Embarked=df.Embarked.astype('category')\n",
    "l=list(df.columns.values)\n",
    "l1=['Pclass','Sex','Fare']\n",
    "l2=['Survived']\n",
    "print (l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Pclass        Fare\n",
      "count  891.000000  891.000000\n",
      "mean     2.308642   32.447545\n",
      "std      0.836071   49.570235\n",
      "min      1.000000    4.012500\n",
      "25%      2.000000    7.925000\n",
      "50%      3.000000   14.454200\n",
      "75%      3.000000   31.000000\n",
      "max      3.000000  512.329200\n"
     ]
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "x_train=df[l1]\n",
    "min_max=MinMaxScaler()\n",
    "print(x_train.describe())\n",
    "x_train=min_max.fit_transform(x_train)\n",
    "#print(x_train.describe())\n",
    "y_train=df[l2]\n",
    "x_test=df1[l1]\n",
    "x_test=min_max.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex\n",
      "0    0.188908\n",
      "1    0.742038\n",
      "Name: Survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "gp=df.groupby('Sex')\n",
    "print(gp['Survived'].agg(np.mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Sex  Survived\n",
      "0   0  0.188908\n",
      "1   1  0.742038\n"
     ]
    }
   ],
   "source": [
    "print(df[['Sex', 'Survived']].groupby('Sex', as_index=False).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=df.f_cnt[df['Survived']==1],bins=(5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'n_neighbors':np.arange(1,50)}\n",
    "knn=KNeighborsClassifier()\n",
    "knn_cv=GridSearchCV(knn,param_grid,cv=2)\n",
    "knn_cv.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8125701459034792\n",
      "{'n_neighbors': 36}\n"
     ]
    }
   ],
   "source": [
    "print (knn_cv.best_score_)\n",
    "print (knn_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=knn_cv.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Survived']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 1 columns):\n",
      "PassengerId    418 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "df2=pd.DataFrame(df1.loc[:,'PassengerId'])\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Survived']=df1['Survived']\n",
    "df2.to_csv('submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=list(df.columns.values)\n",
    "names=['Pclass','Sex','Embarked', 'gp_age', 'f_cnt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso=Lasso(alpha=0.1)\n",
    "x_try=df[names]\n",
    "lasso_coef=lasso.fit(x_try,y_train).coef_\n",
    "_=plt.plot(range(len(names)),lasso_coef)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pclass', 'Sex', 'Embarked', 'gp_age', 'f_cnt']\n"
     ]
    }
   ],
   "source": [
    "print (names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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