code_review.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "315a2589",
   "metadata": {},
   "source": [
    "# Titanic Survival Classification\n",
    "There are a number of issues with the Python and also with the data science in this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "103a473c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn import ensemble\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score\n",
    "\n",
    "df = pd.read_csv('/Users/savsec/Downloads/train.csv')\n",
    "\n",
    "\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ca0a6056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a2741e9",
   "metadata": {},
   "source": [
    "## Clean-up of Missings & Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9968d9c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant = ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6c1d3dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:,relevant]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "80a50c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eliminateMissing(column_name):\n",
    "    if df.loc[:,column_name].dtypes.name == 'float64':\n",
    "        df.loc[:, column_name] = df.loc[:, column_name].fillna(-1)\n",
    "    elif df.loc[:, column_name].dtypes.name == 'object':\n",
    "        df.loc[:, column_name] = df.loc[:, column_name].fillna('missing')\n",
    "\n",
    "eliminateMissing('Age')\n",
    "eliminateMissing('Cabin')\n",
    "eliminateMissing('Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d7d4aeae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Cabin       0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "80b25e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = sklearn.model_selection.train_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2ec46200",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder()\n",
    "def transform_categoricals(df1, column_name):\n",
    "    df1.loc[:, column_name] = le.fit_transform(df1.loc[:, column_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f34293ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      int64\n",
       "Pclass        int64\n",
       "Sex           int64\n",
       "Age         float64\n",
       "SibSp         int64\n",
       "Parch         int64\n",
       "Fare        float64\n",
       "Cabin         int64\n",
       "Embarked      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for j in range(len(list(train))):\n",
    "    if train.iloc[:,j].dtypes.name == 'object':\n",
    "        transform_categoricals(train, train.columns[j])\n",
    "\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5223beda",
   "metadata": {},
   "source": [
    "## Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6dceca2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = ensemble.RandomForestClassifier(n_estimators=500, min_samples_split=0.2, max_features=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f1cbcfeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e6e18acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=None, min_samples_split=0.2,\n",
       "                       n_estimators=500)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.fit(X=train.loc[:, predictors], y=train.Survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c549335",
   "metadata": {},
   "source": [
    "## Out-of-sample Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9439a259",
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in range(len(list(test))):\n",
    "    if test.iloc[:,j].dtypes.name == 'object':\n",
    "        transform_categoricals(test, test.columns[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "57e38a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.Series(m.predict(test.loc[:, predictors]), name='predictions', index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "17d6991b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7668161434977578\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparer = pd.concat([test.Survived, predictions], axis=1)\n",
    "\n",
    "cm = confusion_matrix(comparer.Survived, comparer.predictions)\n",
    "\n",
    "disp=ConfusionMatrixDisplay(cm, display_labels=['Survived', 'Not'])\n",
    "disp.plot()\n",
    "acc= accuracy_score(comparer.Survived, comparer.predictions)\n",
    "print(f\"Accuracy: {acc}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4ea8256",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}