Suicide Rate Prediction.

In the Suicide Rate Prediction project, the descriptive analysis of the dataset was performed to identify the distribution of the features, their relationship with each other, and the potential outliers. 

Feature engineering was conducted to prepare the dataset for the machine learning models. This involved data cleaning, normalization, scaling, and selection of the most relevant features for the models.

Several machine learning algorithms such as  Decision Tree, Random Forest, and Support Vector Machine were used to predict the risk of suicide. The performance of each model was evaluated using various metrics such as accuracy, precision, recall, and F1-score. The best-performing model – Random Forest was selected and its hyperparameters were optimized to improve the performance further.

The results of the machine learning models were analyzed to identify the factors that are most strongly associated with the risk of suicide. The feature importance of each attribute was evaluated, and their impact on the prediction of suicide risk was interpreted. Furthermore, the geographical distribution of suicide risk was visualized using various maps and plots.

The results were interpreted to gain a better understanding of the underlying causes of the increase in suicide rates around the globe.

In conclusion, this project aimed to forecast the risk of suicide and analyze the associated factors responsible for the rise in suicide rates globally. The machine learning models were trained to classify the suicide risk, and the results were interpreted to identify the most significant factors affecting the prediction. The project provided valuable insights that can be used to develop effective prevention strategies.