W50 Saint Palais sur Mer stats & predictions

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<|repo_name|>SureshSampath/ANZ-Data-Challenge<|file_sep|>/README.md # ANZ Data Challenge ## Introduction In this challenge, I have been given data from ANZ bank customers over a period of time. The challenge was to perform EDA on the data given using Python. The dataset is available at https://datahack.analyticsvidhya.com/contest/practice-problem-anz-customer-analytics/ ## Approach I have used pandas for data manipulation, matplotlib & seaborn for visualization & scipy for statistical tests. ## Libraries used * pandas - For data manipulation * numpy - For numerical computation * matplotlib & seaborn - For data visualization * scipy - For statistical tests ## Challenges faced & Solutions adopted ### Challenge #1 : Handling missing values There are two columns in the dataset having missing values - `merchant_id` & `balance`. I have handled them separately. #### Solution #1: merchant_id column Since there are very few missing values in this column, I have decided to impute them using forward fill method. #### Solution #2: balance column Since there are many missing values in this column, I have decided to create a separate column called `balance_missing_flag` which will indicate whether the balance value is missing or not. ### Challenge #2 : Handling dates There are multiple columns in dataset having dates which needs to be converted into datetime format so that we can use them for analysis. #### Solution: I have created a function called `convert_to_datetime` which will convert any given column having date into datetime format. ### Challenge #3 : Handling outliers There are some columns which contain outliers which might affect our analysis if we don't handle them. #### Solution: I have used box plot visualization to identify outliers in each numerical column. After identifying outliers, I have replaced them with mean values. ### Challenge #4 : Handling skewed data There are some columns whose data is skewed. #### Solution: I have used log transformation on these columns so that they become normally distributed. ### Challenge #5 : Identifying multicollinearity among features There might be some features which are highly correlated with each other. If that is the case then we should remove those features since they don't add any value in our analysis. #### Solution: I have calculated correlation between all pairs of features using Pearson correlation coefficient & visualized it using heat map. ### Challenge #6 : Handling categorical variables There are multiple columns in dataset which are categorical variables & we need to convert them into numerical variables so that they can be used for further analysis. #### Solution: I have used label encoding method to convert categorical variables into numerical variables. <|file_sep|># Importing necessary libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats from scipy.stats import norm from scipy.stats import chi2_contingency # Importing warnings library import warnings warnings.filterwarnings('ignore') # Setting figure size plt.rcParams['figure.figsize'] = [15,10] # Reading transactions.csv file transactions = pd.read_csv("transactions.csv") transactions.head() # Reading first_observation.csv file first_observation = pd.read_csv("first_observation.csv") first_observation.head() # Printing shape of transactions dataframe print("Shape of transactions dataframe: ", transactions.shape) # Printing shape of first_observation dataframe print("Shape of first_observation dataframe: ", first_observation.shape) # Printing basic info about transactions dataframe print("Basic info about transactions dataframe: ") transactions.info() # Printing basic info about first_observation dataframe print("Basic info about first_observation dataframe: ") first_observation.info() # Checking number of unique values in each column print("Number of unique values in each column: ") print(transactions.nunique()) print(first_observation.nunique()) # Checking total number of null values in each column print("Total number of null values in each column: ") print(transactions.isnull().sum()) print(first_observation.isnull().sum()) # Checking percentage null values in each column print("Percentage null values in each column: ") print(transactions.isnull().mean()) print(first_observation.isnull().mean()) # Checking how many unique values merchant_id has print("Number of unique merchant_id's: ", transactions.merchant_id.nunique()) # Checking how many unique card_present_flag has print("Number of unique card_present_flag's: ", transactions.card_present_flag.nunique()) # Checking how many unique movement has print("Number of unique movement's: ", transactions.movement.nunique()) # Checking how many unique movement_type has print("Number of unique movement_type's: ", transactions.movement_type.nunique()) # Dropping customer_key from both datasets since it doesn't add any value transactions.drop(['customer_key'], axis=1, inplace=True) first_observation.drop(['customer_key'], axis=1, inplace=True) # Defining a function convert_to_datetime which will take input as a column name def convert_to_datetime(col): try: col = pd.to_datetime(col) return col except: print("Column cannot be converted into datetime format") return col # Converting date columns into datetime format using convert_to_datetime function transactions.date = convert_to_datetime(transactions.date) transactions.auth_date = convert_to_datetime(transactions.auth_date) transactions.posted_date = convert_to_datetime(transactions.posted_date) first_observation.first_active_month = convert_to_datetime(first_observation.first_active_month) # Printing basic info about transactions dataframe after converting date columns into datetime format print("Basic info about transactions dataframe after converting date columns into datetime format: ") transactions.info() # Printing basic info about first_observation dataframe after converting date columns into datetime format print("Basic info about first_observation dataframe after converting date columns into datetime format: ") first_observation.info() def handle_missing_values(df): df['balance_missing_flag'] = np.where(df['balance'].isnull(), 'Missing', 'Not Missing') df['merchant_id'] = df['merchant_id'].fillna(method='ffill') df['balance'] = df['balance'].fillna(df['balance'].mean()) return df def handle_outliers(df): num_cols = df.select_dtypes(include=np.number).columns for col in num_cols: Q1 = df[col].quantile(0.25) Q3 = df[col].quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - (1.5 * IQR) upper_bound = Q3 + (1.5 * IQR) print(col) print(df[df[col] > upper_bound].shape[0]) print(df[df[col] > upper_bound][col].describe()) print('n') if df[df[col] > upper_bound].shape[0] != 0: df.loc[df[col] > upper_bound,col] = df[col].mean() print(df[df[col] > upper_bound].shape[0]) print(df[df[col] > upper_bound][col].describe()) print('n') print('*'*100) print('n') return df def check_skewness(df): num_cols = df.select_dtypes(include=np.number).columns skewness_df = pd.DataFrame(columns=['Feature', 'Skewness']) for col in num_cols: skewness_df.loc[len(skewness_df)] = [col, stats.skew(df[col])] skewness_df.sort_values(by='Skewness', ascending=False, inplace=True) return skewness_df def fix_skewness(df): skewness_df = check_skewness(df) for index,row in skewness_df.iterrows(): if row['Skewness'] > 0.5: print(row['Feature']) print(stats.skew(np.log1p(df[row['Feature']]))) df[row['Feature']] = np.log1p(df[row['Feature']]) print(stats.skew(np.log1p(df[row['Feature']]))) print('*'*100) print('n') return df def plot_boxplot(df): num_cols = df.select_dtypes(include=np.number).columns for col in num_cols: fig, ax = plt.subplots() sns.boxplot(x=df[col], ax=ax) ax.set_title(col) plt.show() def plot_histograms(df): num_cols = df.select_dtypes(include=np.number).columns for col in num_cols: fig, ax = plt.subplots() sns.histplot(x=df[col], kde=True, ax=ax) ax.set_title(col) plt.show() def calculate_pearson_correlation_coefficient(df): corr_matrix = df.corr(method='pearson') corr_matrix.style.background_gradient(cmap='coolwarm') def plot_heatmap(df): corr_matrix = df.corr(method='pearson') sns.heatmap(corr_matrix) def label_encoding(column_name): le_dictionary={} le_dictionary[column_name] = dict(zip(column_name.unique(),range(len(column_name.unique())))) return le_dictionary def apply_label_encoding(le_dictionary,column_name): le_dict_column=le_dictionary[column_name] new_column=[le_dict_column[item] for item in column_name] return new_column def feature_selection(X,y): selector=SelectKBest(f_classif,k='all') selector.fit(X,y) scores=pd.DataFrame(selector.scores_) pvalues=pd.DataFrame(selector.pvalues_) scores.index=X.columns.values.tolist() pvalues.index=X.columns.values.tolist() scores.columns=['Score'] pvalues.columns=['Pvalue'] score_pvalue=pd.concat([scores,pvalues],axis=1) score_pvalue.sort_values(by=['Score'],ascending=False,inplace=True) score_pvalue.reset_index(inplace=True) score_pvalue.rename(columns={'index':'Features'},inplace=True) return score_pvalue def chi_square_test(column_1,column_2): contingency_table=pd.crosstab(column_1,column_2) c,d,e,f=chi2_contingency(contingency_table) critical_value=stats.chi2.ppf(q=0.95,d=e-1) chi_square_statistic=c p_value=d degree_of_freedom=e-1 expected_value=f alpha=0.05 if chi_square_statistic>=critical_value: if p_value<=alpha: chi_square_result="Dependent (reject H0)" else: chi_square_result="Independent (H0 holds true)" else: chi_square_result="Independent (H0 holds true)" return [chi_square_statistic,p_value,critical_value, degree_of_freedom,expected_value,alpha, chi_square_result] handle_missing_values(transactions) handle_missing_values(first_observation) plot_boxplot(transactions) plot_boxplot(first_observation) handle_outliers(transactions) handle_outliers(first_observation) check_skewness(transactions) check_skewness(first_observation) fix_skewness(transactions) fix_skewness(first_observation) plot_histograms(transactions) plot_histograms(first_observation) calculate_pearson_correlation_coefficient(transactions) calculate_pearson_correlation_coefficient(first_observation) plot_heatmap(transactions) plot_heatmap(first_observation) merchant_id_le_dict=label_encoding(transactions.merchant_id) merchant_id_le_dict merchant_id_le_dict=apply_label_encoding(merchant_id_le_dict, transactions.merchant_id) transactions.merchant_id=merchant_id_le_dict card_present_flag_le_dict=label_encoding(transactions.card_present_flag) card_present_flag_le_dict card_present_flag_le_dict=apply_label_encoding(card_present_flag_le_dict, transactions.card_present_flag) transactions.card_present_flag=card_present_flag_le_dict movement_le_dict=label_encoding(transactions.movement) movement_le_dict movement_le_dict=apply_label_encoding(movement_le_dict, transactions.movement) transactions.movement=movement_le_dict movement_type_le_dict=label_encoding(transactions.movement_type) movement_type_le_dict movement_type_le_dict=apply_label_encoding(movement_type_le_dict, transactions.movement_type) transactions.movement_type=movement_type_le_dict first_active_month_le_dict=label_encoding(first_observation.first_active_month) first_active_month_le_dict first_active_month_le_dict=apply_label_encoding(first_active_month_le_dict, first_observation.first_active_month) first_observation.first_active_month=first_active_month_le_dict occupation_category_code_le_dict=label_encoding(first_observation.occupation_category_code) occupation_category_code_le_dict occupation_category_code_le_dict=apply_label_encoding(occupation_category_code_le_dict, first_observation.occupation_category_code) first_observation.occupation_category_code=occupation_category_code_le_dict gender_code_le_dict=label_encoding(first_observation.gender_code) gender_code_le_dict gender_code_le_dict=apply_label_encoding(gender_code_le_dict, first_observation.gender_code) first_observation.gender_code=gender_code_le_dict age_range_code_le_dict=label_encoding(first_observation.age_range_code) age_range_code_le_dict age_range_code_le_dict=apply_label_encoding(age_range_code_le_dict, first_observation.age_range_code) first_observation.age_range_code=age_range_code_le_dict zip_code_prefix_4d_LE_dictionary=label_encoding(first_observation.zip_code_prefix_4d_LE) zip_code_prefix_4d_LE_dictionary zip_code_prefix_4d_LE_dictionary=apply_label_encoding(zip_code_prefix_4d_LE_dictionary, first_observation.zip_code_prefix_4d_LE) first_observation.zip_code_prefix_4d_LE=zip_code_prefix_4d_LE_dictionary zip3_LE_dictionary=label_encoding(first_observation.zip3_LE) zip3_LE_dictionary zip3_LE_dictionary=apply_label_encoding(zip3_LE_dictionary, first_observation.zip3_LE) first_observation.zip3_LE=zip3_LE_dictionary zip3_state_abbrv_LE_dictionary=label_encoding(first_observation.zip3_state_abbrv_LE) zip3_state_abbrv_LE_dictionary zip3_state_abbrv_LE_dictionary=apply_label_encoding(zip3_state_abbrv_LE_dictionary, first_observation.zip3_state_abbrv_LE) first_observation.zip3_state_abbrv_LE=zip3_state_abbrv_LE_dictionary state_abbrv_LE_dictionary=label_encoding(first_observation.state_abbrv_LE) state_abbrv_LE_dictionary state_abbrv_LE_dictionary=apply_label_encoding(state_abbrv_LE_dictionary, first_observation.state_abbrv_LE) first_observation.state_abbrv_LE=state_abbrv_LE_dictionary state_fipscode_numericLE_dictionary=label_encoding(first_observation.state_fipscode_numericLE) state_fipscode_numericLE_dictionary state_fipscode_numericLE_dictionary=apply_label_encoding(state_fipscode_numericLE_dictionary, first_observation.state_fipscode_numericLE) first_observation.state_fipscode_numericLE=state_fipscode_numericLE_dictionary country_iso3166_numericLE_dictionary=label_encoding(first_observation.country_iso3166_numericLE) country_iso3166_numericLE_dictionary country_iso3166_numericLE_dictionary=apply_label_encoding(country_iso3166_numericLE_dictionary, first_observation.country_iso3166_numericLE) first_observation.country_iso3166_numericLE=country_iso3166_numericLE_dictionary