linear regression on linear data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.datasets as datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer=np.random.randn(1,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d3d0abcc0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.distplot(customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "purchases=customer*13+9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d396594e0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "sns.distplot(purchases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have two  variables dependent on each other linearly"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#dividing our data set in two parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx=pd.DataFrame(customer.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfy=pd.DataFrame(purchases.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now creating linear regression model using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(dfx,dfy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=model.predict(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.50431767e+01],\n",
       "       [ 5.39851994e+00],\n",
       "       [ 5.78365838e+00],\n",
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       "       [ 3.37718494e+01],\n",
       "       [ 2.03737089e+01],\n",
       "       [ 1.12774212e+01],\n",
       "       [ 3.04858677e+01],\n",
       "       [ 2.98341633e+01],\n",
       "       [ 5.36566516e+00],\n",
       "       [ 3.47822420e+00],\n",
       "       [ 1.78002764e+01],\n",
       "       [ 4.18479487e+00],\n",
       "       [ 1.92504402e+00],\n",
       "       [-1.78673126e+00],\n",
       "       [ 3.11917855e+01],\n",
       "       [ 4.71713542e+00],\n",
       "       [ 1.68969929e+01],\n",
       "       [-1.14093001e+00],\n",
       "       [ 9.99757203e-02],\n",
       "       [-2.58141576e+00],\n",
       "       [-3.63545559e-01],\n",
       "       [ 1.41608093e+01],\n",
       "       [ 4.38271519e+00],\n",
       "       [ 6.83877830e+00],\n",
       "       [-1.12374250e+00],\n",
       "       [-5.30663250e+00],\n",
       "       [ 1.17353742e+01],\n",
       "       [ 1.72781182e+01],\n",
       "       [ 1.51469591e+01],\n",
       "       [ 1.15595385e+01],\n",
       "       [ 9.73453349e+00],\n",
       "       [-2.69419877e+00],\n",
       "       [ 1.02836565e+00],\n",
       "       [ 2.28048869e+01],\n",
       "       [ 3.15390435e+00],\n",
       "       [ 2.28549940e+01],\n",
       "       [ 3.39629577e+01],\n",
       "       [ 2.21816084e+01],\n",
       "       [ 1.36810526e+01],\n",
       "       [ 1.71252226e-02],\n",
       "       [ 1.41844298e+01],\n",
       "       [ 6.52500254e+00],\n",
       "       [ 5.80813846e+00],\n",
       "       [ 2.11972828e+01],\n",
       "       [ 8.35385373e+00],\n",
       "       [-1.78561610e+01],\n",
       "       [ 1.33852245e+01],\n",
       "       [ 4.13559223e+01],\n",
       "       [ 2.54152687e-01],\n",
       "       [ 4.15098797e+00],\n",
       "       [-8.26061416e+00],\n",
       "       [ 9.81458950e+00],\n",
       "       [ 2.34424301e+01],\n",
       "       [ 1.76790437e+01],\n",
       "       [ 2.86571321e+01],\n",
       "       [ 9.41725617e+00],\n",
       "       [ 1.60815191e+01],\n",
       "       [ 5.24264403e+00],\n",
       "       [ 2.53560829e+01],\n",
       "       [-3.19162675e+01],\n",
       "       [ 2.76010659e+01],\n",
       "       [ 1.93504596e+01],\n",
       "       [-1.93179078e+00],\n",
       "       [ 2.71587497e+01],\n",
       "       [-2.72418254e+00],\n",
       "       [-6.92808664e+00],\n",
       "       [ 2.54341835e+01],\n",
       "       [-7.93259928e+00],\n",
       "       [-9.23377156e-01],\n",
       "       [ 1.25846182e+01],\n",
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       "       [-1.14883773e+01],\n",
       "       [ 1.46651045e+01],\n",
       "       [-3.78774421e+00],\n",
       "       [-2.03580528e+00],\n",
       "       [ 2.26258966e+01],\n",
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       "       [-8.16469622e-01],\n",
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       "       [ 2.64162318e+01],\n",
       "       [ 1.94140021e+01],\n",
       "       [ 6.72567314e+00],\n",
       "       [ 2.72478720e+00],\n",
       "       [ 1.75398065e+01],\n",
       "       [-2.92766239e-01],\n",
       "       [ 2.46272558e+01],\n",
       "       [-9.00962512e+00],\n",
       "       [-1.12826879e+01],\n",
       "       [ 7.97820765e+00],\n",
       "       [-9.47395605e+00],\n",
       "       [ 1.42917703e+01],\n",
       "       [ 2.26407950e+00],\n",
       "       [-1.35562807e+01],\n",
       "       [ 8.48948856e+00],\n",
       "       [ 2.91937752e+01],\n",
       "       [ 3.64775595e+01],\n",
       "       [ 1.61797306e+01],\n",
       "       [ 1.49328431e+01],\n",
       "       [ 1.82591949e+00],\n",
       "       [ 2.62821765e+00],\n",
       "       [ 1.47088421e+01],\n",
       "       [ 1.07376657e+00],\n",
       "       [-6.63554821e+00],\n",
       "       [-4.59438976e+00],\n",
       "       [ 8.57643509e+00],\n",
       "       [ 8.94062062e+00],\n",
       "       [ 1.38046094e+01],\n",
       "       [ 1.83226474e+01],\n",
       "       [ 9.89078543e+00],\n",
       "       [ 1.61606028e+01],\n",
       "       [ 1.06304988e+01],\n",
       "       [ 1.42218743e+01],\n",
       "       [-1.23931355e+01],\n",
       "       [ 1.53185622e+01],\n",
       "       [ 1.21679748e+01],\n",
       "       [ 2.41273255e+01],\n",
       "       [ 1.69383713e+01],\n",
       "       [ 2.34104577e+01],\n",
       "       [ 1.74960404e+01],\n",
       "       [ 1.57576956e+01],\n",
       "       [-4.81083858e+00],\n",
       "       [ 2.31586074e+00],\n",
       "       [ 7.69006764e+00],\n",
       "       [-1.72914798e+01],\n",
       "       [ 1.37956883e+01],\n",
       "       [ 1.23899967e+01],\n",
       "       [ 1.11517890e+01],\n",
       "       [-1.51455004e+00],\n",
       "       [ 4.68299941e+00],\n",
       "       [ 4.27091709e+00],\n",
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       "       [-5.53405501e-01],\n",
       "       [ 8.56497924e+00],\n",
       "       [ 1.06509861e+01]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dfy, ypred,'.')\n",
    "x = np.linspace(0, 330, 100)\n",
    "y = x\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=model.predict([[0.46486]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.46486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0  0.46486"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15.04318]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.043177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "0  15.043177"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfy.head(1)"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}