/Inside-the-Bazaar-Unveiling-the-Lives-of-Local-Shopkeepers

Explore small business owners' lives through ML analysis. Discover insights on age, income, education, mortgage, and more. Models include logistic regression, decision trees, and random forest. Visualize demographics and challenges for future interventions.

Primary LanguagePython

Inside the Bazaar: Unveiling the Lives of Local Shopkeepers

  • Overview: Inside the Bazaar is a machine learning project aimed at understanding the lives of small shop workers through exploratory data analysis (EDA). The project leverages various machine learning models to gain insights into the socioeconomic landscape of local shopkeepers, focusing on pivotal variables such as age, income, education, mortgage, and personal loan status.
  • Key Features: Exploratory Data Analysis (EDA): Gain insights into the demographics, socioeconomic status, and challenges faced by local shop workers.
  • Machine Learning Models: Utilize three different models for prediction and understanding: 1.Logistic Regression 2.Decision Trees 3.Random Forest
  • Importance of Project's Key Features: Understand the significance of variables such as age, income, education, mortgage, and personal loan in analyzing the behavior of small shop workers.
  • Usage Requirements 1.Python 3.x 2.Jupyter Notebook 3.Required Python libraries (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn)