Regression-Project-Time-Series

📊 Regression Project - Time Series Analytics 🚀 Dive into time series data with practical regression implementations. Explore stationarity, decomposition, and modeling using Python tools. Your go-to guide for mastering time series analytics!

Sales Prediction Exploration

Introduction

Welcome to the Sales Prediction Exploration project! This comprehensive analysis delves into the intricacies of time series analysis, offering a step-by-step journey through data loading, exploratory data analysis (EDA), preprocessing, feature engineering, and strategic model applications.

Project Structure

Sales-Prediction-Exploration ├── data │ ├── train.csv │ ├── test.csv │ ├── oil.csv │ └── holidays_events.csv ├── notebook: LP3_Regression_Project 1 │ ├── 01_Exploratory_Data_Analysis.ipynb │ ├── 02_Preprocessing_and_Feature_Engineering.ipynb │ ├── 03_Model_Application.ipynb │ └── ... ├── visuals │ ├── PowerBi Folder │ ├── Autocorrelation.png │ ├── Impact of Promotion on sales.png │ └── ... ├── README.md └── requirements.txt

Data

The data folder encompasses essential datasets for our analysis:

• train.csv: Training data with sales information.
• test.csv: Test data for predicting future sales.
• oil.csv: Daily oil prices, vital for economic insights.
• holidays_events.csv: Information on holidays and events, influencing sales patterns.

Notebook

Explore detailed analyses in the 'notebook':

• `01_Exploratory_Data_Analysis: In-depth exploration of sales data.
• `02_Preprocessing_and_Feature_Engineering: Preprocessing steps and feature engineering.
• `03_Model_Application: Application of One-Hot Encoding, Linear Regression, Hyperparameter Tuning and Random Forests.

Visuals

Discover visual representations in the visuals folder, aiding in understanding complex patterns and trends.

Requirements

The necessary Python packages are outlined in requirements.txt.