Linear_Regression_with_Housing_Prices.ipynb

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    "##Linear regression :Simple Steps\n",
    "\n",
    "###SalePrice prediction from household competition of kaggle. \n",
    "\n"
   ]
  },
  {
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   "source": [
    "#Load packages \n",
    "import pandas as pd \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "data = pd.read_csv('train.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "# create a Python list of feature names \n",
    "required_cols = ['MSSubClass', 'LotArea', 'MiscVal'] \n",
    "X = data[required_cols] \n",
    "X = data[['MSSubClass', 'LotArea', 'MiscVal']]\n",
    "y = data['SalePrice'] \n",
    "y = data.SalePrice\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vivek\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.cross_validation import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# import model \n",
    "from sklearn.linear_model import LinearRegression \n",
    "linreg = LinearRegression() \n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 7,
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    }
   ],
   "source": [
    "# fit model to the training data \n",
    "linreg.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "# make predictions on the testing set \n",
    "y_pred = linreg.predict(X_test) "
   ]
  },
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