velicki / Python_ML_Weather_Predict

Python project for predicting weather conditions using machine learning. Includes data preprocessing, feature engineering, and a Random Forest Classifier to predict clear, cloudy, rain, snow, and fog. Train on historical data and make predictions on new weather data.

Python ML Weather Predict README

A Python project for predicting weather conditions using machine learning.

This project uses historical weather data to predict weather conditions such as clear, cloudy, rain, snow, and fog. It involves data preprocessing, feature engineering, and training a Random Forest Classifier.

---

WHAT I HAVE LEARNED

Throughout the development of this project, I have gained valuable insights and knowledge in several key areas:

1. Data Preprocessing

• Handling Missing Data: I learned various techniques to handle missing data in the dataset, such as imputation and dropping missing values.
• Data Cleaning: Techniques for cleaning and transforming raw data into a usable format for machine learning models.

2. Feature Engineering

• Categorical Encoding: I utilized both Label Encoder and one-hot encoding to handle categorical variables:
  • Label Encoder: Used to convert categorical labels into numerical format, suitable for algorithms like Random Forest.
  • One-Hot Encoding: Used to convert categorical variables with multiple classes into a binary vector.
• Feature Scaling: I applied feature scaling techniques like normalization to standardize the range of independent variables.

3. Machine Learning Model Training and Evaluation

• Model Selection: I chose and implemented the Random Forest Classifier as my predictive model, understanding its strengths and weaknesses.
• Model Training: I learned how to train a machine learning model on training data and tune hyperparameters to optimize performance.
• Model Evaluation: I used various evaluation metrics such as accuracy, precision, recall, and F1-score to assess the model's performance.

4. Real-World Application

• Understanding Weather Data: I gained insights into the characteristics and challenges of weather data, such as variability and seasonal patterns.
• Practical Machine Learning Applications: I applied machine learning techniques to a real-world problem, understanding how they can be used to predict and analyze weather patterns.

5. Continuous Learning and Improvement

• Iterative Development: I iteratively improved the model based on feedback and evaluation results, learning to iterate and refine machine learning projects.
• Research and Experimentation: I explored different algorithms, techniques, and libraries, continuously learning and experimenting to improve model performance.

This project has provided me with valuable practical experience in machine learning, data preprocessing, model evaluation, and software development, enhancing my skills and preparing me for future data science projects.

---

Features

• Data Loading and Cleaning: Load weather data from CSV, handle missing values, and clean the dataset.

• Feature Engineering: One-hot encoding and LABEL ENCODER for categorical variables and preparing the dataset for modeling.

• Model Training: Train a Random Forest Classifier to predict weather conditions.

• Evaluation: Evaluate the model's performance using accuracy, classification reports, and confusion matrices.

• Prediction: Make predictions on new weather data.

---