Project : Youtube Adview Prediction

Data Description : The file train.csv contains metrics and other details of about 15000 youtube videos. videos. The metrics include number of views, likes, dislikes, comments and apart from that published date, duration and category are also included. The train.csv file also contains the metric number of adviews which is our target variable for prediction.

Data :

Training Dataset: "train.csv"

Testing Dataset: "test.csv"

Context : Youtube advertisers pay content creators based on adviews and clicks for the goods and services being marketed. They want to estimate the adview based on other metrics like comments, likes etc. The problem statement is therefore to train various regression models and choose the best one to predict the number of adviews. The data needs to be refined and cleaned before feeding in the algorithms for better results.

Objective : To build a machine learning regression to predict youtube adview count based on other youtube metrics.

Steps and Tasks :

1. Import the datasets and libraries, check shape and datatype.
2. Visualise the dataset using plotting using heatmaps and plots. You can study data distributions for each attribute as well.
3. Clean the dataset by removing missing values and other things.
4. Transform attributes into numerical values and other necessary transformations.
5. Normalise your data and split the data into training, validation and test set in the appropriate ratio.
6. Use linear regression, Support Vector Regressor for training and get errors.
7. Use Decision Tree Regressor and Random Forest Regressors.
8. Build an artificial neural network and train it with different layers and hyperparameters. Experiment a little. Use keras.
9. Pick the best model based on error as well as generalisation. From my experiments, the best model is Support Vector Regression as its error is the least error among all.
10. Save the model and predicted on the test set.