The objective is to predict whether a student will be placed during Campus Recruitement or not.
- Importing our data.
- Checking for null values and finding mean of different columns, their min and max values, and getting information about different columns of our data.
- Visualizing our data in order to find out the best columns to use as features for our model.
- Make a copy of original data to make the changes and seperate out the columns that we will use as our features.
- Feeding these features to three different Models i.e. Logistic Regression, KNN and SVM to get the best results.
- Concluding our analysis by testing the model with some random user input.
The Model were able to make a decent prediction about whether a person will be placed or not. Out of the three, Logistic Regression gave us the best results. These models can still be improved to make more accurate predictions.