PROJECT 1 LEVEL 1  
Idea: Exploratory Data Analysis (EDA) on Retail Sales Data

Description: In this project, you will work with a dataset containing information about retail sales. The goal is
to perform exploratory data analysis (EDA) to uncover patterns, trends, and insights that can help the retail business make informed decisions.

Key Concepts and Challenges:  Data Loading and Cleaning: Load the retail sales dataset.
                              Descriptive Statistics: Calculate basic statistics (mean, median, mode, standard deviation).
                              Time Series Analysis: Analyze sales trends over time using time series techniques.
                              Customer and Product Analysis: Analyze customer demographics and purchasing behavior.
                              Visualization: Present insights through bar charts, line plots, and heatmaps.
                              Recommendations: Provide actionable recommendations based on the EDA.

Learning Objectives: Gain hands-on experience in data cleaning and exploratory data analysis. Develop skills in interpreting descriptive statistics and time series analysis.
Learn to use data visualization for effective communication of insights.

PROJECT 2 LEVEL 1
Idea: Customer Segmentation Analysis

Project Description: The aim of this data analytics project is to perform customer segmentation analysis for an e-commerce company. By analyzing customer behavior and purchase patterns, the goal is to group customers into distinct segments. This segmentation can inform targeted marketing strategies, improve customer satisfaction, and enhance overall business strategies.

PROJECT 1 LEVEL 2
Idea: Predicting House Prices with Linear Regression

Description: The objective of this project is to build a predictive model using linear regression to estimate anumerical outcome based on a dataset with relevant features. Linear regression is afundamental machine learning algorithm, and this project provides hands-on experience in developing, evaluating, and interpreting a predictive model.

Key Concepts and Challenges: Data Collection: Obtain a dataset with numerical features and a target variable for prediction.
                                              Data Exploration and Cleaning: Explore the dataset to understand its structure, handle missing values, and ensure data quality.
                                              Feature Selection: Identify relevant features that may contribute to the predictive model.
                                              Model Training: Implement linear regression using a machine learning library (e.g., Scikit-Learn).
                                              Model Evaluation: Evaluate the model's performance on a separate test dataset using metrics such as Mean Squared Error or R-squared.
                                              Visualization: Create visualizations to illustrate the relationship between the predicted and actual values.