Module_1f_Exercise_LinearRegression.ipynb

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   "source": [
    "# Exercise - Linear Regression\n",
    "\n",
    "Over 370,000 used cars were scraped from Ebay-Kleinanzeigen. The content of the data is in German, so one has to translate it first to English. The data is available [here](https://www.kaggle.com/orgesleka/used-cars-database) The fields included in the file data/autos.csv are:\n",
    "\n",
    "- seller : private or dealer\n",
    "- offerType\n",
    "- vehicleType\n",
    "- yearOfRegistration : at which year the car was first registered\n",
    "- gearbox\n",
    "- powerPS : power of the car in PS\n",
    "- model\n",
    "- kilometer : how many kilometers the car has driven\n",
    "- monthOfRegistration : at which month the car was first registered\n",
    "- fuelType\n",
    "- brand\n",
    "- notRepairedDamage : if the car has a damage which is not repaired yet\n",
    "- price : the price on the ad to sell the car. \n",
    "\n",
    "**Goal**  \n",
    "Given the characteristics/features of the car, the sale price of the car is to be predicted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import the required libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "#Do basic sanity check.\n",
    "#1. Look into the first few records\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#2. What are the column names?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#3. What are the column types?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#4. Do label encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#5. Ideally, we would do some exploratory analysis. \n",
    "#For practice, plot: year of registration vs price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#6.Build OLS Model - sklearn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#7. Report the diagnostics. And discuss the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#8. Build L2 Regression using sklearn - linear_model.Ridge \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#9. Try with different values of alpha (0.001, 0.01, 0.05, 0.1, 0,5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10.\n",
    "The following code is from sklearn [official documentation](http://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py)\n",
    "\n",
    "```\n",
    "# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>\n",
    "# License: BSD 3 clause\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "\n",
    "# X is the 10x10 Hilbert matrix\n",
    "X = 1. / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])\n",
    "y = np.ones(10)\n",
    "\n",
    "#compute paths\n",
    "n_alphas = 200\n",
    "alphas = np.logspace(-10, -2, n_alphas)\n",
    "clf = linear_model.Ridge(fit_intercept=False)\n",
    "\n",
    "coefs = []\n",
    "for a in alphas:\n",
    "    clf.set_params(alpha=a)\n",
    "    clf.fit(X, y)\n",
    "    coefs.append(clf.coef_)\n",
    "\n",
    "#Display results\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('weights')\n",
    "plt.title('Ridge coefficients as a function of the regularization')\n",
    "plt.axis('tight')\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "Can you modify this code to plot for 10 values of alpha and see how weights get changed ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#11. Build a L1 Linear Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#12. Feature selection from L1 Linear Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#13. Find the generalization error.\n",
    "#Split dataset into two: Train and Test : 80% and 20%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#14. Build L2 Regularization model on train and predict on test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#15. Report the RMSE\n"
   ]
  }
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