titanic_ml.ipynb

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      "name": "titanic_ml.ipynb",
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      "name": "python3",
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        "id": "-mt1juLYHRqu",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics\n",
        "import sklearn.ensemble as ske"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "source": [
        "# Importing the dataset\n",
        "titanic_df = pd.read_excel('titanic3.xls', 'titanic3', index_cols=None, na_values=['NA'])\n",
        "titanic_df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24160</td>\n",
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              "      <td>B5</td>\n",
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              "      <td>2</td>\n",
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              "      <td>St Louis, MO</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>male</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
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              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   pclass  survived                                             name     sex  \\\n",
              "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
              "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
              "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
              "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
              "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
              "\n",
              "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
              "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
              "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
              "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
              "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "\n",
              "                         home.dest  \n",
              "0                     St Louis, MO  \n",
              "1  Montreal, PQ / Chesterville, ON  \n",
              "2  Montreal, PQ / Chesterville, ON  \n",
              "3  Montreal, PQ / Chesterville, ON  \n",
              "4  Montreal, PQ / Chesterville, ON  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
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        },
        "outputId": "e93d9d06-4da3-4ae4-e29c-831e183764b6"
      },
      "cell_type": "code",
      "source": [
        "# Checking the overall status of survival\n",
        "# Turns out that only 38% of people survived the disaster\n",
        "titanic_df['survived'].mean()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3819709702062643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "nFHKBMlYaD-C",
        "colab_type": "code",
        "colab": {
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        },
        "outputId": "27fb8ac8-be8f-4725-8ffd-de388fa560ca"
      },
      "cell_type": "code",
      "source": [
        "# Grouping passengers by classes to get a fine view\n",
        "class_sex_grouping = titanic_df.groupby(['pclass', 'sex']).mean()\n",
        "class_sex_grouping"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>female</th>\n",
              "      <td>0.965278</td>\n",
              "      <td>37.037594</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.472222</td>\n",
              "      <td>109.412385</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.340782</td>\n",
              "      <td>41.029250</td>\n",
              "      <td>0.340782</td>\n",
              "      <td>0.279330</td>\n",
              "      <td>69.888385</td>\n",
              "      <td>162.828571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>female</th>\n",
              "      <td>0.886792</td>\n",
              "      <td>27.499191</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.650943</td>\n",
              "      <td>23.234827</td>\n",
              "      <td>52.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.146199</td>\n",
              "      <td>30.815401</td>\n",
              "      <td>0.327485</td>\n",
              "      <td>0.192982</td>\n",
              "      <td>19.904946</td>\n",
              "      <td>171.233333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>female</th>\n",
              "      <td>0.490741</td>\n",
              "      <td>22.185307</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>0.731481</td>\n",
              "      <td>15.324250</td>\n",
              "      <td>183.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.152130</td>\n",
              "      <td>25.962273</td>\n",
              "      <td>0.470588</td>\n",
              "      <td>0.255578</td>\n",
              "      <td>12.415462</td>\n",
              "      <td>151.854167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "               survived        age     sibsp     parch        fare        body\n",
              "pclass sex                                                                    \n",
              "1      female  0.965278  37.037594  0.555556  0.472222  109.412385         NaN\n",
              "       male    0.340782  41.029250  0.340782  0.279330   69.888385  162.828571\n",
              "2      female  0.886792  27.499191  0.500000  0.650943   23.234827   52.000000\n",
              "       male    0.146199  30.815401  0.327485  0.192982   19.904946  171.233333\n",
              "3      female  0.490741  22.185307  0.791667  0.731481   15.324250  183.000000\n",
              "       male    0.152130  25.962273  0.470588  0.255578   12.415462  151.854167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
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        "outputId": "466776a2-45fa-404b-9212-1342c96d9f90"
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      "cell_type": "code",
      "source": [
        "# Visualising the above result\n",
        "class_sex_grouping.plot.bar()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd91f800be0>"
            ]
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          "metadata": {
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          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
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vny5cuKBLly5p586daty4sVOLBQDAUxV5ynr//v2aMWOGEhMTZbFYtGHDBvXq1UtDhw5V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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd91fb20978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VBtti0H8bTw1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "c0401397-b4a9-46ab-8226-6803a803c2fc"
      },
      "cell_type": "code",
      "source": [
        "# Visualising by grouping according to age\n",
        "grouping_by_age = pd.cut(titanic_df['age'], np.arange(0, 90, 10))\n",
        "age_grouping = titanic_df.groupby(grouping_by_age).mean()\n",
        "age_grouping['survived'].plot.bar()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd91f8fdda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAY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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd91cf42588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "csMT4sctc-Ym",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "6e4d02be-131c-4027-cac5-9bd4d8dc2840"
      },
      "cell_type": "code",
      "source": [
        "# Cleaning the data\n",
        "# Get the count of values in dataset\n",
        "titanic_df.count()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass       1309\n",
              "survived     1309\n",
              "name         1309\n",
              "sex          1309\n",
              "age          1046\n",
              "sibsp        1309\n",
              "parch        1309\n",
              "ticket       1309\n",
              "fare         1308\n",
              "cabin         295\n",
              "embarked     1307\n",
              "boat          486\n",
              "body          121\n",
              "home.dest     745\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "WaY1tiGNfvCu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Remove unwanted columns like body, cabin, boat\n",
        "titanic_df = titanic_df.drop(['body', 'cabin', 'boat'], axis = 1)\n",
        "\n",
        "# Remove missing values from home.dest\n",
        "titanic_df['home.dest'] = titanic_df['home.dest'].fillna('NA')\n",
        "titanic_df = titanic_df.dropna()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "S4gqZgK1gkSF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "766e267e-b695-4bd8-8cf2-245109dcf244"
      },
      "cell_type": "code",
      "source": [
        "# Get the count of values in dataset after removing missing values\n",
        "titanic_df.count()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass       1043\n",
              "survived     1043\n",
              "name         1043\n",
              "sex          1043\n",
              "age          1043\n",
              "sibsp        1043\n",
              "parch        1043\n",
              "ticket       1043\n",
              "fare         1043\n",
              "embarked     1043\n",
              "home.dest    1043\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "kfVwlXzFgySm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Preprocess the remaining data\n",
        "def preprocess_titanic_df(df):\n",
        "  processed_df = df\n",
        "  le = preprocessing.LabelEncoder()\n",
        "  processed_df.sex = le.fit_transform(processed_df.sex)\n",
        "  processed_df.embarked = le.fit_transform(processed_df.embarked)\n",
        "  processed_df = processed_df.drop(['name', 'ticket', 'home.dest'], axis = 1)\n",
        "  return processed_df\n",
        "\n",
        "processed_df = preprocess_titanic_df(titanic_df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "edpLcHPekdCt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Splitting dataset into test and train\n",
        "X = processed_df.drop(['survived'], axis = 1).values\n",
        "y = processed_df['survived'].values\n",
        "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ye3A1F1dqLzS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "c1ee1628-ada6-4ee4-d3b4-0ec3ccb0f538"
      },
      "cell_type": "code",
      "source": [
        "# Building a decision tree model\n",
        "model_dt = tree.DecisionTreeClassifier(max_depth=10)\n",
        "model_dt.fit(X_train, y_train)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
              "            max_features=None, max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=1, min_samples_split=2,\n",
              "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "            splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "I3jifFBEsxMX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5cbf56cc-4e63-4202-c851-ca213209c593"
      },
      "cell_type": "code",
      "source": [
        "# Using a shuffle validator to test the accuracies of different models\n",
        "shuffle_validator = cross_validation.ShuffleSplit(len(X), n_iter = 20, test_size = 0.2, random_state = 0)\n",
        "def test_classifier(clf):\n",
        "  scores = cross_validation.cross_val_score(clf, X, y, cv = shuffle_validator)\n",
        "  print(\"Accuracy: %0.4f (+/- %0.2f)\" % (scores.mean(), scores.std()))\n",
        "  \n",
        "# Testing Decision Tree Classifier\n",
        "test_classifier(model_dt)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7749 (+/- 0.02)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U2qwyiDiuyUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "11a30489-1255-48a0-8812-caf8114303c2"
      },
      "cell_type": "code",
      "source": [
        "# Testing Random Forest Classifier\n",
        "model_rf = ske.RandomForestClassifier(n_estimators = 50)\n",
        "test_classifier(model_rf)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7847 (+/- 0.02)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "15EAcTZAvBns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5e86f6eb-a253-4610-cbf0-70dbaf54fbff"
      },
      "cell_type": "code",
      "source": [
        "# Testing Gradient Boosting Classifier\n",
        "model_gb = ske.GradientBoostingClassifier(n_estimators = 50)\n",
        "test_classifier(model_gb)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8201 (+/- 0.02)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KPWTvwgRwRx0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "1e47a35e-6bc6-43f5-8530-591f0cd89605"
      },
      "cell_type": "code",
      "source": [
        "# Using Gradient Boosting Classifier on test data\n",
        "model_gb.fit(X_train, y_train)\n",
        "prediction = model_gb.predict(X_test)\n",
        "prediction"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
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              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    }
  ]
}