https://github.com/urvee1810/eda-time-series
A comprehensive time series analysis of French retail quarterly sales data from 2012 to 2017. The project focuses on analyzing sales patterns, seasonal decomposition, and trend analysis using various statistical techniques and visualizations.
https://github.com/urvee1810/eda-time-series
arima-modeling data-visualization exploratory-data-analysis matplotlib numpy pandas pmdarima python scikit-learn seaborn statsmodels time-series-analysis trend-analysis
Last synced: 2 months ago
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A comprehensive time series analysis of French retail quarterly sales data from 2012 to 2017. The project focuses on analyzing sales patterns, seasonal decomposition, and trend analysis using various statistical techniques and visualizations.
- Host: GitHub
- URL: https://github.com/urvee1810/eda-time-series
- Owner: Urvee1810
- Created: 2025-02-18T12:45:17.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-02-18T13:30:03.000Z (2 months ago)
- Last Synced: 2025-02-18T13:39:26.662Z (2 months ago)
- Topics: arima-modeling, data-visualization, exploratory-data-analysis, matplotlib, numpy, pandas, pmdarima, python, scikit-learn, seaborn, statsmodels, time-series-analysis, trend-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# EDA-Time-series
*This project was completed as part of PG Level Advanced Certification Programme in Computational Data Science coursework at Centre for Continuing Education - Indian Institute of Science in collaboration with Talent Sprint*
A special thanks to Prof. Dr. Shashi Jain & Mentor Mr. Sachin Sharma
Problem Statement: Perform Exploratory Data Analysis (EDA) of Retail Sales time series data using visualizations and statistical methods.
Module: Business Analytics
Project Type: Team
# Learning Outcomes:
- perform Exploratory data analysis (EDA) of the time series
- perform Time series behaviour analysis in qualitative and quantitative terms
- summarize the findings based on the EDA
# Overview
A comprehensive time series analysis of French retail quarterly sales data from 2012 to 2017. The project focuses on analyzing sales patterns, seasonal decomposition, and trend analysis using various statistical techniques and visualizations.
# Key Features
- Quarterly sales trend analysis
- Seasonal pattern identification
- Year-over-year growth analysis
- Time series decomposition
- Detrending and deseasonalization
- Distribution analysis by year
- Lag analysis for sales patterns
# Tools & Technologies
- Python
- Pandas & NumPy
- Statsmodels
- Matplotlib & Seaborn
- SciPy
- Time Series Visualization Tools
# Results
Reveals distinct seasonal patterns in retail sales, with comprehensive analysis of yearly trends, quarterly variations, and growth patterns across different time periods.