The problem statement involves building a predictive model that can forecast future outcomes for a stock of Science and Technology personnel. This could include predicting the number of personnel in this field, the demand for their services, the growth rate of the sector, and other relevant metrics.
The model would require access to historical data on the stock of Science and Technology personnel, as well as any relevant economic indicators or other factors that could impact the growth of this field. The goal of the model would be to provide insights that can inform decision-making for businesses, investors, and policymakers who are interested in this sector.
The predictive model would likely involve using statistical and machine learning techniques to identify patterns and trends in the data, and to make predictions based on these insights. The model would need to be trained on a large dataset of historical data and validated using appropriate techniques to ensure that it is robust and reliable.
Ultimately, the goal of the model would be to provide accurate and reliable forecasts that can be used to inform strategic decision-making for stakeholders in the Science and Technology sector.
The linear regression model is trained on the training data, and the predictions are made on the testing data using the predict function. The predicted values are then printed. Finally, the performance of the model is evaluated using mean squared error and mean absolute error, which provides a measure of how well the model is able to predict the target variable based on the input features. The model is used to forecast future demand for science and technology personnel, which could help companies and organizations better plan their hiring and training strategies. if the model predicts a decrease in the number of Science and Technology personnel, organizations may need to adjust their recruitment strategies or consider outsourcing certain tasks. This model can predict future outcomes with a certain level of accuracy.
Define the Problem: Clearly define the problem that the model is expected to solve.
Data Collection: Collect the data that will be used to train the model. The data should be relevant, representative, and accurate.
Data Preparation: Pre-process the data to prepare it for use in the model. This may include cleaning the data, transforming the data into a suitable format, and splitting the data into training, validation, and test sets.
Feature Engineering: Select the features that will be used as inputs to the model. This may involve transforming the raw data into more meaningful features, selecting relevant features, and scaling the features appropriately.
Model Selection: Select the appropriate model to solve the problem. This will depend on the nature of the problem, the available data, and the desired outcome
Model Evaluation: Evaluate the performance of the model using the validation data. This may involve calculating various metrics such as accuracy, precision, recall, and F1-score.
Model Testing: Test the performance of the final model on the test data. This provides an estimate of how well the model is likely to perform in the real world.
Develop the linear regression model for the heart disease dataset using the scikit-learn a. Divide the data into training (75%) and testing set (25%) b. Analyse the impact of smoking on heart disease and display the intercept and regression coefficients c. Predict the y value (y’) for the testing set (x) d. Analyse the performance metrics with the actual value (y) and predicted values (y’)
Team Members:
Sri Ram M S - URK20AI1043
Leka Shree J - URK20AI1051