Weather Prediction Model | Machine Learning & Python
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
Weather Prediction Model | Machine Learning & Python
The project was initiated to predict weather conditions and the probability of rainfall using historical meteorological data. The main challenge was handling a large dataset and transforming it into a complete system capable of data processing, analysis, model training, and performance evaluation through an easy-to-use interface.
As the Team Leader, I participated in developing the project as an end-to-end solution, starting from data uploading and exploration through preprocessing, model selection, and performance evaluation.
We developed an interactive interface using Streamlit, where users can upload their own datasets in CSV or Excel format and explore the data by viewing the number of rows and columns. The application also provides several visualization options, including Scatter Plot, Box Plot, Line Plot, and Count Plot, helping users understand the data and identify different relationships and patterns.
A flexible data preprocessing pipeline was designed, allowing users to select the appropriate method for each preprocessing stage. The pipeline includes handling missing values using KNN Imputation or Simple Imputation, treating outliers using Clipping or Winsorization, and applying Standard Scaling, MinMax Scaling, or Power Transformation. It also addresses class imbalance using SMOTE or Under-Sampling, with the option to apply PCA for dimensionality reduction. After preprocessing, the data is automatically split into training and testing sets.
The application provides multiple Machine Learning models, allowing users to select and experiment with different algorithms directly through the Streamlit interface. The available models include Logistic Regression, Random Forest, Decision Tree, SVM, and KNN, enabling users to compare different models and select the most suitable one based on evaluation results.
After training, users can evaluate the selected model using Test Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. In one experiment, the Random Forest model achieved a Test Accuracy of 84.44%, along with a detailed performance report and confusion matrix.
Ultimately, we transformed the project from a basic weather prediction model into a complete interactive Streamlit application. The application allows users to upload their own data, explore it visually, select appropriate preprocessing techniques, experiment with different Machine Learning models, and evaluate their results—all through a single, user-friendly interface.
Technologies & Tools
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Streamlit, Machine Learning, Data Preprocessing, Feature Engineering, PCA, SMOTE, Model Evaluation
The project was initiated to predict weather conditions and the probability of rainfall using historical meteorological data. The main challenge was handling a large dataset and transforming it into a complete system capable of data processing, analysis, model training, and performance evaluation through an easy-to-use interface.
As the Team Leader, I participated in developing the project as an end-to-end solution, starting from data uploading and exploration through preprocessing, model selection, and performance evaluation.
We developed an interactive interface using Streamlit, where users can upload their own datasets in CSV or Excel format and explore the data by viewing the number of rows and columns. The application also provides several visualization options, including Scatter Plot, Box Plot, Line Plot, and Count Plot, helping users understand the data and identify different relationships and patterns.
A flexible data preprocessing pipeline was designed, allowing users to select the appropriate method for each preprocessing stage. The pipeline includes handling missing values using KNN Imputation or Simple Imputation, treating outliers using Clipping or Winsorization, and applying Standard Scaling, MinMax Scaling, or Power Transformation. It also addresses class imbalance using SMOTE or Under-Sampling, with the option to apply PCA for dimensionality reduction. After preprocessing, the data is automatically split into training and testing sets.
The application provides multiple Machine Learning models, allowing users to select and experiment with different algorithms directly through the Streamlit interface. The available models include Logistic Regression, Random Forest, Decision Tree, SVM, and KNN, enabling users to compare different models and select the most suitable one based on evaluation results.
After training, users can evaluate the selected model using Test Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. In one experiment, the Random Forest model achieved a Test Accuracy of 84.44%, along with a detailed performance report and confusion matrix.
Ultimately, we transformed the project from a basic weather prediction model into a complete interactive Streamlit application. The application allows users to upload their own data, explore it visually, select appropriate preprocessing techniques, experiment with different Machine Learning models, and evaluate their results—all through a single, user-friendly interface.
Technologies & Tools
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Streamlit, Machine Learning, Data Preprocessing, Feature Engineering, PCA, SMOTE, Model Evaluation
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