dl_admissions_reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e624ec57",
   "metadata": {},
   "source": [
    "## Deep Learning Gradauate School Admission Regression\n",
    "\n",
    "This notebook was built for the Graduate School Admissions Regression Challenge Project for the Codecademy course: Build Deep Learning Models with TensorFlow.\n",
    "\n",
    "We were give a dataset with multiple variables to predict the chance a student will get into graduate school. The dataset can be found here:\n",
    "https://www.kaggle.com/mohansacharya/graduate-admissions?select=Admission_Predict_Ver1.1.csv\n",
    "\n",
    "### Importing necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "86fafd13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267d8a40",
   "metadata": {},
   "source": [
    "### Reading in data and getting a first look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "43644504",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading in data\n",
    "adm_data = pd.read_csv('admissions_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ac9473f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
      "0           1        337          118                  4  4.5   4.5  9.65   \n",
      "1           2        324          107                  4  4.0   4.5  8.87   \n",
      "2           3        316          104                  3  3.0   3.5  8.00   \n",
      "3           4        322          110                  3  3.5   2.5  8.67   \n",
      "4           5        314          103                  2  2.0   3.0  8.21   \n",
      "\n",
      "   Research  Chance of Admit   \n",
      "0         1              0.92  \n",
      "1         1              0.76  \n",
      "2         1              0.72  \n",
      "3         1              0.80  \n",
      "4         0              0.65  \n"
     ]
    }
   ],
   "source": [
    "#looking at the first 5 rows\n",
    "print(adm_data.head())\n",
    "# print(adm_data.columns)\n",
    "# print(adm_data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a15313",
   "metadata": {},
   "source": [
    "(Taken from the codecademy website)  \n",
    "This dataset has nine different columns:  \n",
    "\n",
    "Serial No.: Index of each row (1-500)  \n",
    "GRE Score: GRE test score (out of 340)  \n",
    "TOEFL Score: TOEFL test score (out of 120)  \n",
    "University Rating: Evaluated university rating (out of 5)  \n",
    "SOP: Statement of Purpose Strength (out of 5)  \n",
    "LOR: Letter of Recommendation Strength (out of 5)  \n",
    "CGPA: Undergraduate GPA (out of 10)  \n",
    "Research: Has research experience (either 0 or 1)  \n",
    "Chance of Admit: Applicant’s chance of being admitted (ranging from 0 to 1)  \n",
    "\n",
    "### Checking for NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "909d9c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No.           0\n",
       "GRE Score            0\n",
       "TOEFL Score          0\n",
       "University Rating    0\n",
       "SOP                  0\n",
       "LOR                  0\n",
       "CGPA                 0\n",
       "Research             0\n",
       "Chance of Admit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for NA values\n",
    "adm_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5088b6b6",
   "metadata": {},
   "source": [
    "### Renaming columns as appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4ddad063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['serial no.', 'gre score', 'toefl score', 'university rating', 'sop',\n",
      "       'lor ', 'cgpa', 'research', 'chance of admit '],\n",
      "      dtype='object')\n",
      "Index(['serial_no', 'gre_score', 'toefl_score', 'univ_rating', 'sop', 'lor ',\n",
      "       'cgpa', 'research', 'adm_percent'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#converting columns to lowercase and renaming\n",
    "adm_data.columns = map(str.lower, adm_data.columns)\n",
    "#renaming columns to make accessing them easier\n",
    "print(adm_data.columns)\n",
    "#there is a space at the end of 'chance of admit '\n",
    "adm_data = adm_data.rename(columns={'serial no.': 'serial_no', 'gre score': 'gre_score', 'toefl score': 'toefl_score', 'university rating': 'univ_rating', 'chance of admit ': 'adm_percent' })\n",
    "print(adm_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ed2727",
   "metadata": {},
   "source": [
    "### Quick data exploration \n",
    "\n",
    "Looking at the min and max values of the response(dependent) variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eb966685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.97\n",
      "0.34\n"
     ]
    }
   ],
   "source": [
    "print(adm_data.adm_percent.max())\n",
    "print(adm_data.adm_percent.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fd0194",
   "metadata": {},
   "source": [
    "The TOEFL test is given to non-native English speakers applying to universities. Test Of English as a Foreign Language. It is interesting that this is not metioned in the description for the dataset as this is a particular group of students applying to universities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a7e3ceb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[118 107 104 110 103 115 109 101 102 108 106 111 112 105 114 116 119 120\n",
      "  98  93  99  97 117 113 100  95  96  94  92]\n"
     ]
    }
   ],
   "source": [
    "print(adm_data.toefl_score.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490868ef",
   "metadata": {},
   "source": [
    "Every student has a TOEFL test value, therefore none of these applicants are native English speakers.\n",
    "\n",
    "Want to look at the corelation between all the variables to see if there are any independent variables that are highly correlated with one another, we usually remove these variable, also called multicollinear. Also want to see how these variables are correlated with the dependent variable, 'adm_percent'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e1c18ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pearson Correlation Coefficients')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#removing serial_no. variable\n",
    "correlation = adm_data.iloc[:, 1:]\n",
    "\n",
    "#creating correaltion matrix as heatmap for easy detection of correaltion\n",
    "plt.figure(figsize=(14, 12))\n",
    "ax = sns.heatmap(correlation.corr(), annot = True, vmin=-1, vmax=1, center= 0, cmap=\"Spectral\")\n",
    "ax.set_title('Pearson Correlation Coefficients')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38b0557",
   "metadata": {},
   "source": [
    "None of our variables are multicollinear so we can use all of them as predictors. It is also seen that all of the independent variables are mig-highly correlated with our dependent variable. Looks like we are on the right track!\n",
    "\n",
    "Looking at the pair plot between all variables to look at the relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3737146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fef6fe3df10>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.pairplot(correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e68ec5",
   "metadata": {},
   "source": [
    "They all look to have postive linear relationships with one another, except 'research' as this is more categorical."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd123ff",
   "metadata": {},
   "source": [
    "### Creating feature and label datasets\n",
    "\n",
    "We are trying to predict the probability that someone will get into graduate school, therefore, we are trying to predict the 'adm_percent' or percent change of admission.\n",
    "\n",
    "Every column is numerical so no reason to perform one-hot encoding.\n",
    "Each column except 'serial_no' looks to be significant to the data analysis, I am not going to include this column in my analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b35f859",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data into feature and label sets\n",
    "labels = adm_data.iloc[:,-1]\n",
    "#print(labels)\n",
    "features = adm_data.iloc[:, 1:-1]\n",
    "print(features.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e60b48",
   "metadata": {},
   "source": [
    "### Splitting data into train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43774392",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Splitting data into train and test datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "train, test, train_labels, test_labels = train_test_split(features, labels, test_size = 0.20, random_state = 42)\n",
    "print(len(test))\n",
    "print(len(train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e70810",
   "metadata": {},
   "source": [
    "### Data Standardization\n",
    "\n",
    "As seen in the descriptions earlier, the data is on multiple scales. The best way to deal with this so as to work well with any model is to normalize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "617e0323",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardizng all numerical columns in the dataset. In this case, that is all columns of data\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "num_features = features.select_dtypes(include=['float64', 'int64'])\n",
    "num_cols = num_features.columns\n",
    "ct = ColumnTransformer([('standardize', StandardScaler(), num_cols)], remainder = 'passthrough')\n",
    "train_scaled = ct.fit_transform(train)\n",
    "test_scaled = ct.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f20336",
   "metadata": {},
   "source": [
    "### Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d78ee4de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using tf Sequential to start a model\n",
    "from tensorflow.keras.models import Sequential\n",
    "reg_model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894f9846",
   "metadata": {},
   "source": [
    "### Input layer\n",
    "\n",
    "This layer has the same number of nodes as features in your data, an additional node can be added as a bias term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b78806c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating an input layer with the number of features in our dataset\n",
    "#Some neural networks add an additional node here for a bias term\n",
    "from tensorflow.keras import layers\n",
    "num_features = train_scaled.shape[1]\n",
    "input = layers.InputLayer(input_shape=(num_features,))\n",
    "reg_model.add(input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61deac1",
   "metadata": {},
   "source": [
    "### Hidden Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb6cab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding hidden layer with 64 neurons\n",
    "reg_model.add(layers.Dense(64, activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1147fe0a",
   "metadata": {},
   "source": [
    "### Output layer\n",
    "\n",
    "Regression models have one output node as they are continous variables and not categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee4bc7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding output layer\n",
    "reg_model.add(layers.Dense(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b464ed",
   "metadata": {},
   "source": [
    "### Backpropagation and model compilation\n",
    "\n",
    "Compiling the model with loss metrics and the SGD optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e744bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing optimizer and compiling model\n",
    "#from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "opt = SGD(learning_rate=0.01)\n",
    "\n",
    "reg_model.compile(loss='mae',  metrics=['mean_squared_error'], optimizer=opt)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8843d119",
   "metadata": {},
   "source": [
    "### Model fit and evaluation with early stopping\n",
    "\n",
    "Using early stopping to find number of epochs. Too many will lead to overfitting and too few will lead to underfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85805bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "#Using early stopping to find number of epochs to train. Too many will lead to overfitting and too few will lead to \n",
    "#underfitting\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
    "history = reg_model.fit(train_scaled, train_labels, epochs=500, batch_size=1, verbose=0, validation_split = 0.2, callbacks = [es])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d020bc",
   "metadata": {},
   "source": [
    "Plotting figures that show the MSE and MAE loss metrics and show where the model employed the early stopping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f712daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating figure area for 2 plots side-by-side\n",
    "fig, axs = plt.subplots(1,2, figsize=(15, 8))\n",
    "plt.suptitle('Loss functions and Number of Epochs with Early-Stopping')\n",
    "(ax1, ax2) = axs\n",
    "\n",
    "#first plot MAE vs epochs\n",
    "ax1.plot(history.history['loss'], label='train')\n",
    "ax1.plot(history.history['val_loss'], label='validation')\n",
    "ax1.legend(loc=\"upper right\")\n",
    "ax1.set_xlabel('Number of Epochs')\n",
    "ax1.set_ylabel('MAE')\n",
    "\n",
    "#second plot MSE vs epochs\n",
    "ax2.plot(history.history['mean_squared_error'], label='train')\n",
    "ax2.plot(history.history['val_mean_squared_error'], label='validation')\n",
    "ax2.legend(loc=\"upper right\")\n",
    "ax2.set_xlabel('Number of Epochs')\n",
    "ax2.set_ylabel('MSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172f25ac",
   "metadata": {},
   "source": [
    "### Model Evaluation with R-Squared, MSE, and MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37a5388d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#model evaluation with mae, and mse\n",
    "res_mae, res_mse = reg_model.evaluate(test_scaled, test_labels, verbose=0)\n",
    "\n",
    "res_mse = round(res_mse, 5)\n",
    "res_mae = round(res_mae, 4)\n",
    "print(res_mse, res_mae)\n",
    "\n",
    "#getting predicted values\n",
    "predictions = reg_model.predict(test_scaled)\n",
    "#calculating R^2 using the predicted values and actual labels.\n",
    "r2 = round(r2_score(test_labels, predictions) * 100, 2)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65dd66a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('MAE is ' + f'{res_mse:.{4}f}' + ' which means if we were to randomly look at a datapoint, our prediction would be ' + f'{res_mse:.{4}f}' ' off from the    actual value.')\n",
    "print('Great! The model explains ' + f'{r2:.{0}f}' + '% of the variance in the chance of admissions percentage.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7fb1fcf",
   "metadata": {},
   "source": [
    "### MAE Baseline\n",
    "\n",
    "The dummy regressor is used to predict a simple baseline for the test data. In this case, I have used median, so the dummy regressor uses the train data to predict the median of the test data. This serves as a baseline in which to compare the mae found using our regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7de49ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "dummy_regr = DummyRegressor(strategy='median')\n",
    "dummy_regr.fit(train_scaled, train_labels)\n",
    "y_pred = dummy_regr.predict(test_scaled)\n",
    "mae_baseline = round(mean_absolute_error(test_labels, predictions), 5)\n",
    "print(mae_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2af8eb4",
   "metadata": {
    "variables": {
     "mae_baseline": {},
     "res_mae": {}
    }
   },
   "outputs": [],
   "source": [
    "print('Our model gave an mae of  ' + f'{res_mae:.{4}f}' + '  and our baseline is  ' + f'{mae_baseline:.{4}f}' + ' , so we met the baseline parameter.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21590d4",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "\n",
    "The best model found could only make the baseline MAE value, but explained a lot of the variance in our model.\n",
    "\n",
    "I will continue to play with different hyperpaprameters during this course to understand how I can get better predictions in the future."
   ]
  }
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