E-commerce businesses often struggle with inaccurate or incomplete customer data, which can lead to inefficiencies in marketing campaigns, customer service interactions, and fraud detection efforts.
Implementing EDA techniques allows e-commerce businesses to identify and correct data anomalies in customer records, such as missing values, invalid formats, and outliers.
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A 20% reduction in customer data errors, leading to increased accuracy in marketing campaigns, customer segmentation, and fraud detection.
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A 15% improvement in customer service efficiency, as accurate customer data enables faster resolution of inquiries and issue
I'm a full stack developer experienced in deploying artificial intelligence powered apps
- dataprofessor
- Pandas Profiling in Data Science
Live demo
Install required packages
pip install streamlit
pip install pycaret
pip insatll scikit-learn==0.23.2
pip install numpy
pip install seaborn
pip install pandas
pip install matplotlib
pip install plotly-express
pip install streamlit-lottie- Drop your Datasets in the app to get resuilts
- you can use he exaple data provided in the app
import streamlit as st
import pandas as pd
import plotly.express as px
import base64
from io import StringIO, BytesIO
import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
from pandas_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report
def app():
st.markdown('''
# **Exploratory data analysis App**
Please upload your xlsx file or click the button below to use example dataset
---
''')
# Upload CSV data
with st.sidebar.header('Upload your XLSX data'):
uploaded_file = st.sidebar.file_uploader("Upload your input XLSX file", type=["xlsx"])
# Pandas Profiling Report
if uploaded_file is not None:
@st.cache
def load_csv():
csv = pd.read_excel(uploaded_file,engine='openpyxl')
#csv = pd.read_csv(uploaded_file,encoding='latin1', index_col=None,usecols = "A,B,C,D,E,F,H,G,H,I,J")
return csv
df = load_csv()
pr = ProfileReport(df, explorative=True)
st.header('**Input DataFrame**')
st.write(df)
st.write('---')
st.header('**Exploratory data analysis Report**')
st_profile_report(pr)
else:
st.info('Awaiting for XLSX file to be uploaded.')
if st.button('Press to use Example Dataset'):
# Example data
@st.cache
def load_data():
a = pd.DataFrame(
np.random.rand(100, 5),
columns=['a', 'b', 'c', 'd', 'e']
)
return a
df = load_data()
pr = ProfileReport(df, explorative=True)
st.header('**Input DataFrame**')
st.write(df)
st.write('---')
st.header('**Exploratory data analysis Report**')
st_profile_report(pr)
To deploy this project we used streamlit to create Web App
- Run this code below
streamlit run app.py Happy Coding!!!!!!