dradecic

# STEP 1
BASE_DIR = 'images/'

# STEP 2
SUB_DIRS = [topic + '/' for topic in df['Topic'].unique()]

# Print a message to the user
print('Image Download Started...')
start_time = datetime.datetime.now()

dradecic

@app.route('/', methods=['GET', 'POST'])
def chart():
    selected_class = request.form.get('dropdown-select')

    if selected_class == 0 or selected_class == None:
        survived_chart, title_chart, hist_age = redraw(1)
    else:
        survived_chart, title_chart, hist_age = redraw(selected_class)

    script_survived_chart, div_survived_chart = components(survived_chart)

dradecic

X = data.drop('Survived', axis=1)
target = data['Survived']

rfc = RandomForestClassifier(random_state=101)
rfecv = RFECV(estimator=rfc, step=1, cv=StratifiedKFold(10), scoring='accuracy')
rfecv.fit(X, target)

dradecic

def calculate_woe_iv(dataset, feature, target):
    lst = []
    for i in range(dataset[feature].nunique()):
        val = list(dataset[feature].unique())[i]
        lst.append({
            'Value': val,
            'All': dataset[dataset[feature] == val].count()[feature],
            'Good': dataset[(dataset[feature] == val) & (dataset[target] == 0)].count()[feature],
            'Bad': dataset[(dataset[feature] == val) & (dataset[target] == 1)].count()[feature]
        })

dradecic

correlated_features = set()
correlation_matrix = data.drop('Survived', axis=1).corr()

for i in range(len(correlation_matrix.columns)):
    for j in range(i):
        if abs(correlation_matrix.iloc[i, j]) > 0.8:
            colname = correlation_matrix.columns[i]
            correlated_features.add(colname)

dradecic

b0, b1 = 0.0, 1.0

lr = 0.001
epochs = 10000
error = []

# run 10000 times
for epoch in range(epochs):
    # initialize to 0 -> cost of epoch, Jb_0, Jb_1
    epoch_cost, cost_b0, cost_b1 = 0, 0, 0

dradecic

def get_books(topic_list):
    
    # Generate full URLs from the argument provided by user
    all_urls = []
    for topic in topic_list:
        all_urls.append('http://books.toscrape.com/catalogue/category/books/{}/index.html'.format(topic))

    # Instantiate an empty list for holding the dictionary objects
    all_books = []

dradecic

ratings_df = pd.DataFrame()
ratings_df['Mean_Rating'] = data.groupby('title')['rating'].mean().values
ratings_df['Num_Ratings'] = data.groupby('title')['rating'].count().values


fig, ax = plt.subplots(figsize=(14, 7))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_title('Rating vs. Number of Ratings', fontsize=24, pad=20)
ax.set_xlabel('Rating', fontsize=16, labelpad=20)

dradecic

data.sort_values(by='numRatings', ascending=False).drop_duplicates('movieId')[:10]

dradecic

num_ratings = pd.DataFrame(data.groupby('movieId').count()['rating']).reset_index()
data = pd.merge(left=data, right=num_ratings, on='movieId')
data.rename(columns={'rating_x': 'rating', 'rating_y': 'numRatings'}, inplace=True)