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   "source": [
    "Assignment 4 of the course “Introduction to Machine Learning” at the University of Leoben.\n",
    "Author: Fotios Lygerakis\n",
    "Semester: SS 2022/2023"
   ],
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "source": [
    "Import the libraries"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Create the Regression Models"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "class Predictor:\n",
    "    def __init__(self):\n",
    "        self.coefficients = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        pass\n",
    "\n",
    "    def predict(self, X):\n",
    "        pass\n",
    "\n",
    "class LinearRegression(Predictor):\n",
    "    pass\n",
    "\n",
    "class RidgeRegression(Predictor):\n",
    "    pass\n",
    "\n",
    "class LassoRegression(Predictor):\n",
    "    pass"
   ],
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "source": [
    "Data Preprocessing and Data loading functions"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "def preprocess(df):\n",
    "    pass\n",
    "\n",
    "def train_test_split(X, y, test_size=0.2):\n",
    "    pass\n",
    "\n",
    "def load_data():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# Load the diabetes dataset\n",
    "df = pd.read_csv(\"diabetes.csv\")\n",
    "\n",
    "# Preprocess the dataset\n",
    "df = preprocess(df)"
   ],
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "source": [
    "Load the data"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot unpack non-iterable NoneType object",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[5], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# Load the data\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m X_train, X_test, y_train, y_test \u001B[38;5;241m=\u001B[39m load_data()\n",
      "\u001B[0;31mTypeError\u001B[0m: cannot unpack non-iterable NoneType object"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "X_train, X_test, y_train, y_test = load_data()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Fit the models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Fit the linear regression\n",
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Fit the ridge regression\n",
    "ridge_regression = RidgeRegression(alpha=1)\n",
    "ridge_regression.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Fit the lasso regression\n",
    "lasso_regression = LassoRegression(alpha=1, num_iters=10000, lr=0.001)\n",
    "lasso_regression.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Evaluate the models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
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  }
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