ML-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([1,4,9,16,25])\n",
    "x=x.reshape(-1,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=LinearRegression()\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.30769231, 12.07692308])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y=pred.predict(x_test)\n",
    "pred_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly=PolynomialFeatures(degree=2)\n",
    "\n",
    "poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  2.,  4.],\n",
       "       [ 1.,  3.,  9.],\n",
       "       [ 1.,  4., 16.],\n",
       "       [ 1.,  5., 25.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_poly=poly.fit_transform(x)\n",
    "x_poly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.fit(x_poly,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  9., 16., 25.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=pred.predict(x_poly)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16, 25])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x=np.array([8]).reshape(-1,1)\n",
    "#x_poly=poly.fit_transform(x)\n",
    "#y_pred=pred.predict(x_poly)\n",
    "#y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.772360450213455e-15"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "er=mean_absolute_error(y,y_pred)\n",
    "er\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\csonusmahae\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\csonusmahae\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "import seaborn as sns\n",
    "%matplotlib inline \n",
    "ax1=sns.distplot(x,hist=False,color='yellow',label='absoloute')\n",
    "sns.distplot(y_pred,hist=False,color='green',label='predicted',ax=ax1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y,y_pred)\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}