Chemical Classification using Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.read_csv(\"Logistic_X_train.csv\")\n",
    "y=pd.read_csv(\"Logistic_Y_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python37\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\program files\\python37\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "<p>600 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "      label\n",
       "470       1\n",
       "1225      0\n",
       "386       1\n",
       "2352      0\n",
       "416       1\n",
       "674       1\n",
       "191       0\n",
       "1810      1\n",
       "839       0\n",
       "2428      0\n",
       "131       0\n",
       "2035      1\n",
       "1369      0\n",
       "918       1\n",
       "2127      1\n",
       "1294      0\n",
       "2206      0\n",
       "737       0\n",
       "719       1\n",
       "57        1\n",
       "1607      0\n",
       "2954      1\n",
       "2105      1\n",
       "995       0\n",
       "1147      1\n",
       "2655      1\n",
       "2045      0\n",
       "1574      0\n",
       "568       0\n",
       "620       1\n",
       "...     ...\n",
       "727       0\n",
       "991       0\n",
       "2238      1\n",
       "8         1\n",
       "1020      0\n",
       "2421      0\n",
       "577       1\n",
       "760       0\n",
       "2646      1\n",
       "1011      0\n",
       "2518      1\n",
       "2001      1\n",
       "2015      0\n",
       "821       0\n",
       "129       1\n",
       "2589      0\n",
       "1389      1\n",
       "67        0\n",
       "1836      0\n",
       "2487      0\n",
       "2735      0\n",
       "207       0\n",
       "1129      0\n",
       "72        1\n",
       "214       1\n",
       "2340      1\n",
       "819       0\n",
       "1981      0\n",
       "506       1\n",
       "1102      0\n",
       "\n",
       "[600 rows x 1 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9983333333333333\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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