Titanic_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "441e8086-74cb-4c26-add8-f4ca88faa7df",
   "metadata": {},
   "source": [
    "<font size=\"+3\"><strong>Titanic - Machine Learning from Disaster </strong></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964df389-c76a-4e46-bc36-d9883e2f52d4",
   "metadata": {},
   "source": [
    "The project is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.\n",
    "\n",
    "The sinking of the Titanic is one of the most infamous shipwrecks in history.\n",
    "\n",
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n",
    "\n",
    "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
    "\n",
    "In this projet, we are going to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bfd80e-2db8-474c-bd8d-50c54372cc7d",
   "metadata": {},
   "source": [
    "# Importation of libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eebb3942-5ff5-497e-9aee-c8f9a9872d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px \n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from category_encoders import OrdinalEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4a1b36-fb06-4eba-9ca6-9d3748c7553a",
   "metadata": {},
   "source": [
    "# Data Importation and Preparation/Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70d462a-03e9-4dae-850d-a9e533e18ecd",
   "metadata": {},
   "source": [
    "## Create a \"data_wranging\" function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e701d3a8-90a1-41d7-9118-5e52073ea28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_wrangling(filepath):\n",
    "    # import \"csv file\" into DataFrame\n",
    "    df = pd.read_csv(filepath, index_col = \"PassengerId\")\n",
    "    \n",
    "    # Dropping Low and High cardinality columns\n",
    "    drop_cols =  [\"Name\", \"Ticket\", \"Cabin\", \"Embarked\"]\n",
    "    df = df.drop(columns = drop_cols)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec63dc7c-03ef-4843-9bcc-227f8bbf86e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data_wrangling(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "51dceb95-a0d9-46c9-852b-3739da984d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4812c587-0c40-4a33-9957-8ebd79cf368a",
   "metadata": {},
   "source": [
    "# Split Dataset into Feature Matrix and Target Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ddea0085-864a-4abe-b805-179ae9e54434",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df.drop(columns = \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "36279ec5-fb76-4c1a-8690-c2e00b005e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df[\"Survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec40a5c-e2d1-4b46-8760-ece083e2f2b7",
   "metadata": {},
   "source": [
    "# Model selection and Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a41fc765-73a0-4821-a866-c12618ee0cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                 OrdinalEncoder(cols=[&#x27;Sex&#x27;],\n",
       "                                mapping=[{&#x27;col&#x27;: &#x27;Sex&#x27;, &#x27;data_type&#x27;: dtype(&#x27;O&#x27;),\n",
       "                                          &#x27;mapping&#x27;: male      1\n",
       "female    2\n",
       "NaN      -2\n",
       "dtype: int64}])),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;randomforestclassifier&#x27;,\n",
       "                 RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ordinalencoder&#x27;,\n",
       "                 OrdinalEncoder(cols=[&#x27;Sex&#x27;],\n",
       "                                mapping=[{&#x27;col&#x27;: &#x27;Sex&#x27;, &#x27;data_type&#x27;: dtype(&#x27;O&#x27;),\n",
       "                                          &#x27;mapping&#x27;: male      1\n",
       "female    2\n",
       "NaN      -2\n",
       "dtype: int64}])),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;randomforestclassifier&#x27;,\n",
       "                 RandomForestClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(cols=[&#x27;Sex&#x27;],\n",
       "               mapping=[{&#x27;col&#x27;: &#x27;Sex&#x27;, &#x27;data_type&#x27;: dtype(&#x27;O&#x27;),\n",
       "                         &#x27;mapping&#x27;: male      1\n",
       "female    2\n",
       "NaN      -2\n",
       "dtype: int64}])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ordinalencoder',\n",
       "                 OrdinalEncoder(cols=['Sex'],\n",
       "                                mapping=[{'col': 'Sex', 'data_type': dtype('O'),\n",
       "                                          'mapping': male      1\n",
       "female    2\n",
       "NaN      -2\n",
       "dtype: int64}])),\n",
       "                ('simpleimputer', SimpleImputer()),\n",
       "                ('standardscaler', StandardScaler()),\n",
       "                ('randomforestclassifier',\n",
       "                 RandomForestClassifier(random_state=42))])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = make_pipeline(\n",
    "    OrdinalEncoder(),\n",
    "    SimpleImputer(strategy=\"mean\"),\n",
    "    StandardScaler(),\n",
    "    RandomForestClassifier(random_state=42)\n",
    ")\n",
    "model.fit(X_train,y_train\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b613df3-8831-4d30-aef8-d43a68ead796",
   "metadata": {},
   "source": [
    "## Calculate the accuracy of the Model using train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e37ccdfb-c0a0-4090-b937-698bd9283448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9809203142536476"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2e39b3ec-fb2d-4cd4-88c0-ddd43ca35ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08364522, 0.26564807, 0.26756316, 0.04772491, 0.03795474,\n",
       "       0.2974639 ])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e40b5e52-3f99-4108-85a7-ccccd6c51826",
   "metadata": {},
   "source": [
    "# Testing the built Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8ea9f6-7d85-4380-a9eb-c16e728dc671",
   "metadata": {},
   "source": [
    "## Import test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8865da5-e24f-4b2e-b56b-bb1433590403",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = data_wrangling(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5d893bb-385f-4428-bbc5-648f3b29a057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9809203142536476"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ae92e920-811d-4944-8369-8149ae842b58",
   "metadata": {},
   "source": [
    "### Make Predictions using test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "33933729-ee99-41ba-b215-16af124c3e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a5dcc344-3f42-4a51-a733-6e4e8f72e0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = model.named_steps[\"randomforestclassifier\"].feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2677d734-ae82-4d51-8d61-69b44468d000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.Series(importances, index = X_test.columns, name = \"Feature_importances\").sort_values()\n",
    "feat_imp.plot(kind = \"barh\")\n",
    "plt.title(\"Feature Importance Plot\")\n",
    "plt.xlabel(\"Coefficients of each feature\")\n",
    "plt.ylabel(\"Features\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35cf9492-f34e-4f33-96e1-b3543d1be0aa",
   "metadata": {},
   "source": [
    "Create a DataFrame using \"PassengerId\" and Prediction values from test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6fafdb1b-f640-4ec3-88c8-e7f2ae31c6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame(\n",
    "    {\"PassengerId\": X_test.index, \"Survived\": predictions}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2ca39643-2af8-49fb-818b-6ab351c6ab13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         1\n",
       "3          895         1\n",
       "4          896         0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112b5a30-063f-4afa-90a2-f33028219bb2",
   "metadata": {},
   "source": [
    "Convert Dataframe to \"CSV\" file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "774a459c-61d0-44eb-a61b-c79c138bc618",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv(\"submission.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a513dc06-8d34-42d8-838e-14821625bfa0",
   "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": "3.9.7"
  }
 },
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