Age Prediction model(mature leaf).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "da1 = pd.read_csv('/home/siddhant/Downloads/Ch Data 1.csv')\n",
    "da2 = pd.read_csv('/home/siddhant/Downloads/Ch Data 2.csv')\n",
    "da3 = pd.read_csv('/home/siddhant/Downloads/Ch Data 3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2b3c49a90>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "da1.plot('Age', ' Winter',style = '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = da1.iloc[:, 0:1].values \n",
    "X1 = da1.iloc[:, 1:2].values \n",
    "y2 = da2.iloc[:, 0:1].values \n",
    "X2 = da2.iloc[:, 1:2].values \n",
    "y3 = da3.iloc[:, 0:1].values \n",
    "X3 = da3.iloc[:, 1:2].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression \n",
    "lin1 = LinearRegression() \n",
    "lin2 = LinearRegression() \n",
    "lin3 = LinearRegression() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin1.fit(X1, y1)\n",
    "lin2.fit(X2, y2)\n",
    "lin3.fit(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Visualising the Linear Regression results \n",
    "plt.scatter(X1, y1, color = 'blue') \n",
    "  \n",
    "plt.plot(X1, lin1.predict(X1), color = 'red') \n",
    "plt.title('Linear Regression') \n",
    "plt.xlabel('Chlorophyll Content in Winter') \n",
    "plt.ylabel('Age') \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the season in which the leaf is taken: \n",
      "Enter 1 for Winter, 2 for Early Spring, 3 for Late Spring\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Enter the season in which the leaf is taken: \")\n",
    "print(\"Enter 1 for Winter, 2 for Early Spring, 3 for Late Spring\")\n",
    "opt = int(input())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[84.78450768]]\n"
     ]
    }
   ],
   "source": [
    "chloro_cont = 340\n",
    "if (opt == 1):\n",
    "    print(lin1.predict(chloro_cont))\n",
    "elif (opt == 2):\n",
    "    print(lin2.predict(chloro_cont))\n",
    "elif (opt == 3):\n",
    "    print(lin3.predict(chloro_cont))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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