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Linkedin Assignment - Scenario 4 -- Predicting titanic survial #952

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Linkedin Assignment - Scenario 4 -- Predicting titanic survial #952

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154 changes: 133 additions & 21 deletions ...earch-and-development/titanic-assignment/01-predicting-survival-titanic-assignement.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -464,9 +464,9 @@
"metadata": {},
"outputs": [],
"source": [
"vars_num = # fill your code here\n",
"vars_num = [var for var in data.columns if data[var].dtype != 'O' and var != target]\n",
"\n",
"vars_cat = # fill your code here\n",
"vars_cat = [var for var in data.columns if data[var].dtype == 'O']\n",
"\n",
"print('Number of numerical variables: {}'.format(len(vars_num)))\n",
"print('Number of categorical variables: {}'.format(len(vars_cat)))"
Expand All @@ -486,7 +486,8 @@
"outputs": [],
"source": [
"# first in numerical variables\n",
"\n"
"\n",
"data[vars_num].isnull().mean()"
]
},
{
Expand All @@ -496,7 +497,8 @@
"outputs": [],
"source": [
"# now in categorical variables\n",
"\n"
"\n",
"data[vars_cat].isnull().mean()"
]
},
{
Expand All @@ -511,7 +513,11 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# determine cardinality of categorical variables\n",
"\n",
"data[vars_cat].nunique().sort_values(ascending=False)"
]
},
{
"cell_type": "markdown",
Expand All @@ -525,7 +531,13 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# display numerical variables distributions\n",
"\n",
"data[vars_num].hist(bins=30, figsize=(12, 10))\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -565,7 +577,12 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# extract the letter from the cabin\n",
"\n",
"for dataset in [X_train, X_test]:\n",
" dataset['cabin'] = dataset['cabin'].str[0]"
]
},
{
"cell_type": "markdown",
Expand All @@ -582,7 +599,21 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# add missing indicator and fill numerical variables with median\n",
"\n",
"for dataset in [X_train, X_test]:\n",
" for var in vars_num:\n",
" if dataset[var].isnull().sum() > 0:\n",
" dataset[var + '_na'] = np.where(dataset[var].isnull(), 1, 0)\n",
"\n",
"# capture the median values from train set\n",
"train_median = X_train[vars_num].median()\n",
"\n",
"# fill missing values in train and test\n",
"X_train[vars_num] = X_train[vars_num].fillna(train_median)\n",
"X_test[vars_num] = X_test[vars_num].fillna(train_median)"
]
},
{
"cell_type": "markdown",
Expand All @@ -596,21 +627,34 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# replace missing values in categorical variables with string Missing\n",
"\n",
"for dataset in [X_train, X_test]:\n",
" dataset[vars_cat] = dataset[vars_cat].fillna('Missing')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# check that there are no missing values left\n",
"\n",
"X_train.isnull().mean().sort_values(ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# display categorical variable cardinality after missing value replacement\n",
"\n",
"X_train[vars_cat].nunique().sort_values(ascending=False)"
]
},
{
"cell_type": "markdown",
Expand All @@ -626,21 +670,43 @@
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# find frequent labels (>=5%) for each categorical variable\n",
"\n",
"def find_frequent_labels(df, var, rare_perc):\n",
" tmp = df[var].value_counts(normalize=True)\n",
" return tmp[tmp >= rare_perc].index\n",
"\n",
"for var in vars_cat:\n",
" frequent_ls = find_frequent_labels(X_train, var, 0.05)\n",
"\n",
" X_train[var] = np.where(X_train[var].isin(frequent_ls), X_train[var], 'Rare')\n",
" X_test[var] = np.where(X_test[var].isin(frequent_ls), X_test[var], 'Rare')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# check new category cardinality\n",
"\n",
"X_train[vars_cat].nunique().sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# visualize categorical frequencies\n",
"\n",
"for var in vars_cat:\n",
" print(X_train[var].value_counts(normalize=True))\n",
" print()"
]
},
{
"cell_type": "markdown",
Expand All @@ -657,28 +723,45 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# one hot encode categorical variables\n",
"\n",
"X_train = pd.get_dummies(X_train, columns=vars_cat, drop_first=True)\n",
"X_test = pd.get_dummies(X_test, columns=vars_cat, drop_first=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# align train and test set columns\n",
"\n",
"X_train, X_test = X_train.align(X_test, join='left', axis=1, fill_value=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# check shape after encoding\n",
"\n",
"X_train.shape, X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# display first rows\n",
"\n",
"X_train.head()"
]
},
{
"cell_type": "markdown",
Expand All @@ -694,7 +777,18 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# set up the scaler\n",
"\n",
"scaler = StandardScaler()\n",
"\n",
"# fit on train set only\n",
"scaler.fit(X_train)\n",
"\n",
"# transform the train and test set\n",
"X_train = scaler.transform(X_train)\n",
"X_test = scaler.transform(X_test)"
]
},
{
"cell_type": "markdown",
Expand All @@ -711,7 +805,14 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# set up the model\n",
"\n",
"logit = LogisticRegression(C=0.0005, random_state=0, max_iter=200)\n",
"\n",
"# train the model\n",
"logit.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
Expand All @@ -731,7 +832,18 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# make predictions\n",
"\n",
"pred = logit.predict(X_test)\n",
"\n",
"# probabilities for roc-auc\n",
"pred_proba = logit.predict_proba(X_test)[:, 1]\n",
"\n",
"# determine metrics\n",
"print('roc-auc: {}'.format(roc_auc_score(y_test, pred_proba)))\n",
"print('accuracy: {}'.format(accuracy_score(y_test, pred)))"
]
},
{
"cell_type": "markdown",
Expand Down