- Made CSV File and received it using python.
- Made training systems and used it to predict and found the error between the predicted value and the actual value.
- Used linear regression training models, logarithmic models, and other training models to predict.
Python -imported SKLearn, Pandas, etc
I got 0 percent for f1-score for every column, and found the error. I had to fix the trainX value and trainY value from the topic column in the csv file.
1.Successfully trained models and used it to predict
2. Built a simple recommendation system
3. Organized (cleaned) my data well in the csv file.
- I cleaned my csv file and received it using python in visual studio and made separate variables to store the information.
- Although it was not of high accuracy, my model was successful in predicting the values with less error.
- I eventually printed f1-score, precision, accuracy, etc using my model.