Suicide Rate Prediction Project using Python #shorts

Suicide Rate Prediction Project using Python | Python Final Year Project 2024

Features:-

Data Collection & Preprocessing:-
Gather historical data on suicide rates, demographics, and socio-economic factors.
Clean and normalize the dataset.

Feature Selection:-
Use factors like age, gender, GDP, unemployment, mental health indicators, and regional data.

Exploratory Data Analysis (EDA):-
Visualize data patterns with graphs (histograms, correlation heatmaps).
Identify and treat outliers.

Model Selection:-
Apply models like Linear Regression, Random Forest, Decision Trees, or XGBoost.

Model Training & Evaluation:-
Split data into training/testing sets and optimize using cross-validation.
Evaluate with metrics like Mean Squared Error, R-squared, or ROC curve.

Handling Imbalanced Data:-
Use techniques like SMOTE to balance the dataset.

Prediction & Visualization:-
Visualize predictions with charts and geospatial maps.

Model Deployment:-
Deploy using Flask/Django, with dashboards for insights (Streamlit/Dash).

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