croprec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pylab\n",
    "from sklearn import preprocessing\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gui\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Location=input(\"Enter the location :\")\n",
    "Soil = input(\"Enter the soil :\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>SOIL</th>\n",
       "      <th>STATE</th>\n",
       "      <th>Rice</th>\n",
       "      <th>Cotton</th>\n",
       "      <th>Sugarcane</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Millets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aluvial</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Red</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laterite</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mountain</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SOIL        STATE  Rice  Cotton  Sugarcane  Wheat  Millets\n",
       "0   Aluvial  Maharashtra     1       0          0      1        0\n",
       "1     Black  Maharashtra     0       1          0      0        0\n",
       "2       Red  Maharashtra     0       0          1      0        0\n",
       "3  Laterite  Maharashtra     0       0          0      0        1\n",
       "4  Mountain  Maharashtra     0       0          0      0        0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic=pd.read_csv('location_Avail.csv')\n",
    "basic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>SOIL</th>\n",
       "      <th>STATE</th>\n",
       "      <th>Rice</th>\n",
       "      <th>Cotton</th>\n",
       "      <th>Sugarcane</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Millets</th>\n",
       "      <th>SOIL_C</th>\n",
       "      <th>STATE_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aluvial</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Red</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laterite</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mountain</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Desert</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aluvial</td>\n",
       "      <td>Gujrat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SOIL        STATE  Rice  Cotton  Sugarcane  Wheat  Millets  SOIL_C  \\\n",
       "0   Aluvial  Maharashtra     1       0          0      1        0       0   \n",
       "1     Black  Maharashtra     0       1          0      0        0       1   \n",
       "2       Red  Maharashtra     0       0          1      0        0       5   \n",
       "3  Laterite  Maharashtra     0       0          0      0        1       3   \n",
       "4  Mountain  Maharashtra     0       0          0      0        0       4   \n",
       "5    Desert  Maharashtra     0       0          0      0        0       2   \n",
       "6   Aluvial       Gujrat     1       1          1      1        1       0   \n",
       "\n",
       "   STATE_C  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "5        1  \n",
       "6        0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = preprocessing.LabelEncoder()\n",
    "x1 = basic[\"SOIL\"]\n",
    "y1 = basic[\"STATE\"]\n",
    "x = le.fit_transform(x1)\n",
    "y = le.fit_transform(y1)\n",
    "basic1 = basic.merge(pd.DataFrame({'SOIL_C':x,'STATE_C':y}),right_index=True,left_index = True)\n",
    "basic1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [1, 1],\n",
       "       [1, 5],\n",
       "       [1, 3],\n",
       "       [1, 4],\n",
       "       [1, 2],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = basic1[['STATE_C','SOIL_C']].values\n",
    "Y = basic1[['Rice','Cotton','Sugarcane','Wheat','Millets']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Maharshtra'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-d54bf28c1d32>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mLocation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mSoil\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zinga\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    428\u001b[0m         \"\"\"\n\u001b[0;32m    429\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'tree_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m         \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zinga\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[1;34m\"\"\"Validate X whenever one tries to predict, apply, predict_proba\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    392\u001b[0m             if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[0;32m    393\u001b[0m                                 X.indptr.dtype != np.intc):\n",
      "\u001b[1;32mc:\\users\\zinga\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    494\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m                 \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32mc:\\users\\zinga\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Maharshtra'"
     ]
    }
   ],
   "source": [
    "a = classifier.predict([[Location,Soil]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rice\n",
      "Wheat\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2.36818182])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,5,1):\n",
    "    if a[0][i] == 1:\n",
    "        if i == 0:\n",
    "            print(\"Rice\")\n",
    "            crop=\"Rice\"\n",
    "            data = pd.read_csv('Cropdata.csv')\n",
    "            #location=input(\"Enter the location :\")\n",
    "            # crop=input(\"Enter the crop  :\")\n",
    "            data1=data.query('STATE == \"Maharashtra\" and CROP == @crop')\n",
    "            regressor=LinearRegression()\n",
    "            X=data1[['YEAR']]\n",
    "            Y=data1['YEILD']\n",
    "            regressor.fit(X,Y)\n",
    "            b = regressor.predict([[2018]])\n",
    "        elif i == 1:\n",
    "            print(\"Cotton\")\n",
    "            data = pd.read_csv('Cropdata.csv')\n",
    "            crop=\"Cotton\"\n",
    "            #location=input(\"Enter the location :\")\n",
    "            # crop=input(\"Enter the crop  :\")\n",
    "            data2=data.query('STATE == \"Maharashtra\" and CROP == @crop')\n",
    "            regressor=LinearRegression()\n",
    "            X=data2[['YEAR']]\n",
    "            Y=data2['YEILD']\n",
    "            regressor.fit(X,Y)\n",
    "        elif i == 2:\n",
    "            print(\"Sugarcane\")\n",
    "            crop=\"Sugarcane\"\n",
    "            data = pd.read_csv('Cropdata.csv')\n",
    "            #location=input(\"Enter the location :\")\n",
    "            # crop=input(\"Enter the crop  :\")\n",
    "            data3=data.query('STATE == \"Maharashtra\" and CROP == @crop')\n",
    "            regressor=LinearRegression()\n",
    "            X=data3[['YEAR']]\n",
    "            Y=data3['YEILD']\n",
    "            regressor.fit(X,Y)\n",
    "        elif i == 3:\n",
    "            print(\"Wheat\")\n",
    "            crop=\"Wheat\"\n",
    "            data = pd.read_csv('Cropdata.csv')\n",
    "            #location=input(\"Enter the location :\")\n",
    "            # crop=input(\"Enter the crop  :\")\n",
    "            data4=data.query('STATE == \"Maharashtra\" and CROP == @crop')\n",
    "            regressor=LinearRegression()\n",
    "            X=data4[['YEAR']]\n",
    "            Y=data4['YEILD']\n",
    "            regressor.fit(X,Y)\n",
    "            c = regressor.predict([[2018]])\n",
    "        elif i == 4:\n",
    "            print(\"Millets\")\n",
    "            crop=\"Millets\"\n",
    "            data = pd.read_csv('Cropdata.csv')\n",
    "            #location=input(\"Enter the location :\")\n",
    "            # crop=input(\"Enter the crop  :\")\n",
    "            data5=data.query('STATE == \"Maharashtra\" and CROP == @crop')\n",
    "            regressor=LinearRegression()\n",
    "            X=data5[['YEAR']]\n",
    "            Y=data5['YEILD']\n",
    "            regressor.fit(X,Y)\n",
    "c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got scalar array instead:\narray=1.5.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-82-a1c6603efb94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \"\"\"\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m    206\u001b[0m                                dense_output=True) + self.intercept_\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    512\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m    515\u001b[0m             \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=1.5.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "regressor.predict(1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>YEAR</th>\n",
       "      <th>STATE</th>\n",
       "      <th>CROP</th>\n",
       "      <th>YEILD</th>\n",
       "      <th>COST OF CULTIVATION</th>\n",
       "      <th>PROFIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9743.05</td>\n",
       "      <td>5562.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.2</td>\n",
       "      <td>10523.56</td>\n",
       "      <td>7829.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.3</td>\n",
       "      <td>11568.99</td>\n",
       "      <td>6791.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.4</td>\n",
       "      <td>10908.52</td>\n",
       "      <td>8742.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10783.33</td>\n",
       "      <td>8905.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.6</td>\n",
       "      <td>11031.59</td>\n",
       "      <td>3423.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.7</td>\n",
       "      <td>11873.21</td>\n",
       "      <td>6678.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.8</td>\n",
       "      <td>12523.24</td>\n",
       "      <td>7854.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>1.9</td>\n",
       "      <td>12323.17</td>\n",
       "      <td>12233.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7891.07</td>\n",
       "      <td>2313.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Rice</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10674.34</td>\n",
       "      <td>5672.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR        STATE  CROP  YEILD  COST OF CULTIVATION    PROFIT\n",
       "0   2008  Maharashtra  Rice    1.1              9743.05   5562.90\n",
       "1   2009  Maharashtra  Rice    1.2             10523.56   7829.89\n",
       "2   2010  Maharashtra  Rice    1.3             11568.99   6791.09\n",
       "3   2011  Maharashtra  Rice    1.4             10908.52   8742.12\n",
       "4   2012  Maharashtra  Rice    1.5             10783.33   8905.98\n",
       "5   2013  Maharashtra  Rice    1.6             11031.59   3423.77\n",
       "6   2014  Maharashtra  Rice    1.7             11873.21   6678.88\n",
       "7   2015  Maharashtra  Rice    1.8             12523.24   7854.90\n",
       "8   2016  Maharashtra  Rice    1.9             12323.17  12233.89\n",
       "9   2017  Maharashtra  Rice    2.0              7891.07   2313.42\n",
       "10  2018  Maharashtra  Rice    2.1             10674.34   5672.26"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = pd.read_csv('Cropdata.csv')\n",
    "# # location=input(\"Enter the location :\")\n",
    "# # crop=input(\"Enter the crop  :\")\n",
    "# data1=data.query('STATE == @location and CROP == @crop')\n",
    "# data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "X=data1[['YEAR']]\n",
    "Y=data1['YEILD']\n",
    "regressor.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X,Y)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test =train_test_split(X,Y,test_size =0.20,random_state=0)\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_train,Y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual  Predicted\n",
      "4     1.5        1.5\n",
      "9     2.0        2.0\n",
      "2     1.3        1.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result=pd.DataFrame({'Actual':Y_test,'Predicted':Y_pred})\n",
    "print(result)\n",
    "plt.plot(X_test,Y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}