Shirlyngit/NAIROBI-HOUSE-PRICE-PREDICTION

Built a machine learning system to predict the house prices for different houses, apartments and rentals in Nairobi Metropolitan area - Kenya. ## FLASK_APP SCREENSHOT # 🏡NAIROBI-HOUSE-PRICE-PREDICTION-SYSTEM OBJECTIVE: Build a Predictive Machine Learning System to predict the house prices in different demographic areas within Nairobi Metropolitan area - Kenya. Then build a ChatBot for user engagement with this Housing prices information. 🛠️ Tools, Frameworks, and Technologies: - Python - Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch - Data Manipulation: Pandas, Numpy - Visualization: Matplotlib - Web Frameworks: Flask, FastAPI, or Streamlit. Used Flask - APIs: OpenAI APIs 🔍 Project Overview: 1. Web Scraping: Scrape relevant sites to gather house data. 2. Data Cleaning: Clean and preprocess the data. 3. Exploratory Data Analysis (EDA): Analyze and visualize the data. 4. Modeling: Build and train the predictive models. 5. Deployment: Deploy the model using a web framework. 6. Chatbot Creation: Develop an RAG chatbot, using OpenAI APIs, for user engagement that provides housing information in Kenya. 🗂️ Repository Structure: - 📁 data: Main folder housing all other subfolders. - 🤖 chatbot: Subfolder containing the implementation details of the chatbot. - 🧹 cleaning_eda_modeling: Contains Jupyter notebooks for data cleaning, EDA, and modeling. Also includes the cleaned CSV file ready for modeling. - 📂 data_collection: Subfolder with web scraping scripts and the gathered data. - 🚀 inferencing_and_deployment: Contains inferencing scripts, the flask_app.py script, and the respective requirements.txt file. - 💾 model_preprocessor_weights: Contains the base model and fine-tuned model pickle files along with the preprocessor pickle file. - 🚧 Project Status: For more detailed explanations, refer to the README.md files within each subfolder. 🚀 Skills Utilized & Acquired: 🐍 Python for Data Science: Leveraged Python's powerful libraries for data manipulation, analysis, and modeling. 🌐 Web Scraping: Extracted valuable data from relevant real estate websites. 🧠 Domain Knowledge & Feature Engineerin …