Predicting Salary of a Speific District using Literacy Rate and Population of that District.
Preprocess the Data: Organizing data in a tabular format with two columns: literacy rate and average salary. Ensuring the data is clean and remove any outliers or missing values.
Split the Data: Spliting the dataset into two parts: a training set and a test set. The training set will be used to train the model, while the test set will be used to evaluate its performance.
Choose a Model: In this case, we can use linear regression as a simple model. Linear regression assumes a linear relationship between the input variables (literacy rate) and the output variable (average salary). There are various libraries you can use to implement linear regression, such as scikit-learn in Python.
Train the Model: Fitting the linear regression model to your training data. The model will learn the relationship between the literacy rate and average salary.
Evaluate the Model: Once the model is trained, we can use the test set to evaluate its performance. Calculate metrics such as mean squared error (MSE) or R-squared to assess how well the model predicts the average salary.
Make Predictions: Once the model is deemed satisfactory, we can use it to make predictions on new data. Provide the literacy rate of a district as input to the model, and it will predict the corresponding average salary.
Interpret the Results.
The new trained model is then used to predict the unseen data.