Financial-Inclusion-Data-Analysis

imported necessary files

import matplotlib.pyplot as plt %matplotlib inline import numpy as np import pandas as pd import seaborn as sns from scipy.stats import ttest_1samp from scipy.stats import ttest_ind import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.linear_model import LinearRegression from sklearn import model_selection from sklearn.metrics import r2_score from sklearn.model_selection import train_test_split import statsmodels.api as sm from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

loading files and gettting the preview of the data using pandas

creating hypothesis

Ho: An Individual has a bank account H1:An individual has no bank account

cleaning dataset

making the columns to be uniform Removing duplicates Removing missing values Removing outliers

visualization

I plotted the following scatter plots bar plots pie charts line graphs

Creating a model

I created a model using RandomForestclassifier and logistic reggresion

reduction method

Principle component Analysis Linear reggresion Linear Discriminant Analysis

Conclusion

The best reduction method was LDA with accuracy of 88.89% predictions Histogram