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import streamlit as st |
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import pandas as pd |
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from analysis import univariate_analysis, BivariateAnalysis, multivariate_analysis |
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def eda(): |
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st.image("assets/eda.webp",width=300) |
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st.title("Exploratory Data Analysis") |
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data = pd.read_csv("extracted/customer_churn_dataset-training-master.csv") |
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data.drop("CustomerID",axis = 1,inplace = True) |
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data.dropna(axis=0,inplace = True,how = "all") |
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st.header("Dataset Overview") |
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st.dataframe(data.head()) |
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st.subheader("Select Analysis Type") |
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analysis_type = st.selectbox( |
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"Select Analysis Type", |
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["Univariate Analysis", "Bivariate Analysis", "Multivariate Analysis"] |
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) |
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if analysis_type == "Univariate Analysis": |
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st.subheader("Univariate Analysis") |
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column = st.selectbox("Select a column for univariate analysis", data.columns, key="uni") |
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plot_type = st.selectbox("Select plot type", ["Histogram", "Boxplot", "Pie Chart", "Bar Plot"], key="uni_plot") |
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if st.button("Generate Univariate Plot", key="uni_button"): |
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if column: |
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univariate_analysis(data, column, plot_type) |
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else: |
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st.warning("Please select a column for analysis.") |
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elif analysis_type == "Bivariate Analysis": |
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st.subheader("Bivariate Analysis") |
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column_x = st.selectbox("Select X-axis column", data.columns, key="bi_x") |
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column_y = st.selectbox("Select Y-axis column", data.columns, key="bi_y") |
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plot_type = st.selectbox("Select plot type", ["Scatter Plot", "Bar Plot", "Boxplot"], key="bi_plot") |
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if st.button("Generate Bivariate Plot", key="bi_button"): |
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if pd.api.types.is_numeric_dtype(data[column_x]) and pd.api.types.is_numeric_dtype(data[column_y]): |
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analysis = BivariateAnalysis() |
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analysis.numerical_vs_numerical(data, column_x, column_y, plot_type) |
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elif pd.api.types.is_categorical_dtype(data[column_x]) and pd.api.types.is_categorical_dtype(data[column_y]): |
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analysis = BivariateAnalysis() |
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analysis.numerical_vs_categorical(data, column_x, column_y, plot_type) |
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elif pd.api.types.is_numeric_dtype(data[column_x]) and pd.api.types.is_categorical_dtype(data[column_y]): |
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analysis = BivariateAnalysis() |
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analysis.numerical_vs_categorical(data, column_x, column_y, plot_type) |
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elif pd.api.types.is_categorical_dtype(data[column_x]) and pd.api.types.is_numeric_dtype(data[column_y]): |
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analysis = BivariateAnalysis() |
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analysis.numerical_vs_categorical(data, column_x, column_y, plot_type) |
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elif pd.api.types.is_numeric_dtype(data[column_x]) and pd.api.types.is_numeric_dtype(data[column_y]): |
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analysis = BivariateAnalysis() |
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analysis.numerical_vs_numerical(data, column_x, column_y, plot_type) |
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else: |
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st.warning("Please select numerical columns for analysis. Only numerical data types are supported for this plot.") |
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elif analysis_type == "Multivariate Analysis": |
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data = pd.read_csv("/home/sarath_kumar/customer_chrun_prediction/processed_data/processed_data.csv") |
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st.subheader("Multivariate Analysis") |
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columns = st.multiselect("Select columns for multivariate analysis", data.columns) |
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plot_type = st.selectbox( |
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"Select plot type for multivariate analysis", |
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["Correlation Heatmap", "Scatter Matrix"] |
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) |
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if st.button("Generate Multivariate Plot", key="multi_button"): |
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if columns: |
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multivariate_analysis(data, columns, plot_type) |
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else: |
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st.warning("Please select columns for multivariate analysis.") |
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if __name__ == "__main__": |
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eda() |
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