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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Let’s Find Out How to Make Engaging Videos?

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prshntkmr112 Last Updated : 22 Mar, 2022
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

In this article, we will be predicting how engaging a video can be at the user level. We have been provided with a dataset that contains that user’s earlier videos engagement score along with their personal information.

We will build multiple regression models and see which one performs better.

Contents

  1. Understanding Data
  2. Industrial Applications
  3. Exploratory Data Analysis
  4. Feature Engineering
  5. Model Building
  6. Model Performance
  7. Code
  8. Conclusion

Understanding Data

This data was provided in a “Job-a-thon” contest organized at Analytics Vidya in February 2022. This data was available to those members who have participated in the contest. Since I have participated in the contest I was able to download the dataset. Here I will be presenting my approach to solving the problem.

Features

 

Engaging videos

Here age, gender, and profession are the user information provided to us and engagement_score represents how engaging their video was based on the scale of 1 to 5.

We are not allowed to use the row_id as a feature or build the new features using the row_id.

Industrial Applications

This problem is a day-to-day problem in today’s digital world.

Exploratory Data Analysis

user_id, category_id, and video_id are the categorical features in the dataset even though they are represented in numerical form.

Number of Unique Users in the data

df['user_id'].nunique()
Output : 27734

There are 27734 users in the given data.

Shape of Data

df.shape
Output : (89197, 10)

There are around 90,000 rows in the data and 10 columns including the target column.

Distribution of Numerical Features

num_features=['age','followers','views']
cat_features=['user_id','category_id','video_id','gender','profession']
df[num_features].hist(figsize=(10,10));

Source: Author

We can observe that there are mostly young users in the dataset. The age group between 15 to 25 is at its peak.

Distribution of Categorical Features

There is not much that we can get from user_id, category_id, and video_id. Let’s see the distribution of the ‘gender’ and ‘profession’.

sns.catplot(data=df[cat_features],kind='count',x='profession');

Source: Author

sns.catplot(data=df[cat_features],kind='count',x='gender');

Source: Author

We can observe that there is more number of video entries by ‘Male‘ users and ‘Students‘ is the most frequent profession and ‘Working Professionals’ are least active as per the given data.

Feature Correlation

plt.figure(figsize=(20,10))
sns.heatmap(df.corr(),fmt='g',annot=True)
Engaging videos

Source: Author

We can observe that there are two pair of features that shows some correlation. video_id, category_id and views, followers.

But there is no feature that shows a strong correlation with engament_score.

Pair Plot

sns.pairplot(df)
Pair Plot | Engaging videos

Source: Author

Pair plots also confirm the same that there is no strong correlation of engagement_score with any given feature.

Let’s see if we can build features that have a better correlation with the target (engagemnet_score) and help build a better predictive model.

Feature Engineering

We will try to create features using the followers, view, and engagement_score. We will find out how many min, mean, and max views and followers a user has. We will also find out min, mean, and max views and followers per category.

We will also build the features similarly using engagement_score. This will give us the min, max, and mean engagement_score of user and user per category.

First, let’s split the data into train and test datasets.

Splitting the dataset into train and test data

We are splitting the data into 80-20 split. 80% of the data will be used for training and 20% of the data will be used for testing.

features=['user_id', 'category_id', 'video_id', 'age', 'gender', 'profession',
       'followers', 'views']
target=['engagement_score']
x_train,x_test,y_train,y_test=train_test_split(df[features],df[target],test_size=0.2,random_state=42)
df_train=pd.concat([x_train,y_train],axis=1)
df_test=pd.concat([x_test,y_test],axis=1)

Building Features using ‘followers’ and ‘views’

grp_by=['user_id']
user_id_features=df_train.groupby(grp_by)['followers'].min().reset_index()
user_id_features.rename(columns={'followers':'min_followers'},inplace=True)
user_id_features['avg_followers']=df_train.groupby(grp_by)['followers'].mean().reset_index()['followers']
user_id_features['max_followers']=df_train.groupby(grp_by)['followers'].max().reset_index()['followers']
user_id_features['sum_followers']=df_train.groupby(grp_by)['followers'].sum().reset_index()['followers']
user_id_features['min_views']=df_train.groupby(grp_by)['views'].min().reset_index()['views']
user_id_features['mean_views']=df_train.groupby(grp_by)['views'].mean().reset_index()['views']
user_id_features['max_views']=df_train.groupby(grp_by)['views'].max().reset_index()['views']
user_id_features['sum_views']=df_train.groupby(grp_by)['views'].sum().reset_index()['views']
grp_by1=['user_id','category_id']
user_id_features1=df_train.groupby(grp_by1)['followers'].min().reset_index()
user_id_features1.rename(columns={'followers':'min_followers_category'},inplace=True)
user_id_features1['avg_followers_category']=df_train.groupby(grp_by1)['followers'].mean().reset_index()['followers']
user_id_features1['max_followers_category']=df_train.groupby(grp_by1)['followers'].max().reset_index()['followers']
user_id_features1['sum_followers_category']=df_train.groupby(grp_by1)['followers'].sum().reset_index()['followers']
user_id_features1['min_views_category']=df_train.groupby(grp_by1)['views'].min().reset_index()['views']
user_id_features1['mean_views_category']=df_train.groupby(grp_by1)['views'].mean().reset_index()['views']
user_id_features1['max_views_category']=df_train.groupby(grp_by1)['views'].max().reset_index()['views']
user_id_features1['sum_views_category']=df_train.groupby(grp_by1)['views'].sum().reset_index()['views']
df_train=pd.merge(df_train,user_id_features,how='left',on=grp_by)
df_test=pd.merge(df_test,user_id_features,how='left',on=grp_by)
df_train=pd.merge(df_train,user_id_features1,how='left',on=grp_by1)
df_test=pd.merge(df_test,user_id_features1,how='left',on=grp_by1)

Building Features using ‘engagement_score’

grp_by=['user_id']
user_id_features2=df_train.groupby(grp_by)['engagement_score'].min().reset_index()
user_id_features2.rename(columns={'engagement_score':'min_eng_score'},inplace=True)
user_id_features2['avg_eng_score']=df_train.groupby(grp_by)['engagement_score'].mean().reset_index()['engagement_score']
user_id_features2['max_eng_score']=df_train.groupby(grp_by)['engagement_score'].max().reset_index()['engagement_score']
user_id_features2['sum_eng_score']=df_train.groupby(grp_by)['engagement_score'].sum().reset_index()['engagement_score']
grp_by1=['user_id','category_id']
user_id_features3=df_train.groupby(grp_by1)['engagement_score'].min().reset_index()
user_id_features3.rename(columns={'engagement_score':'min_eng_score_category'},inplace=True)
user_id_features3['avg_eng_score_category']=df_train.groupby(grp_by1)['engagement_score'].mean().reset_index()['engagement_score']
user_id_features3['max_eng_score_category']=df_train.groupby(grp_by1)['engagement_score'].max().reset_index()['engagement_score']
user_id_features3['sum_eng_score_category']=df_train.groupby(grp_by1)['engagement_score'].sum().reset_index()['engagement_score']
df_train=pd.merge(df_train,user_id_features2,how='left',on=grp_by)
df_test=pd.merge(df_test,user_id_features2,how='left',on=grp_by)
df_train=pd.merge(df_train,user_id_features3,how='left',on=grp_by1)
df_test=pd.merge(df_test,user_id_features3,how='left',on=grp_by1)

Now, let’s observe the correlation of new features. We will not include the old features since we already saw that there was no strong correlation with the target column ‘engagement_score’.

new_features=['min_followers',
       'avg_followers', 'max_followers', 'sum_followers', 'min_views',
       'mean_views', 'max_views', 'sum_views', 'min_followers_category',
       'avg_followers_category', 'max_followers_category',
       'sum_followers_category', 'min_views_category', 'mean_views_category',
       'max_views_category', 'sum_views_category', 'min_eng_score',
       'avg_eng_score', 'max_eng_score', 'sum_eng_score',
       'min_eng_score_category', 'avg_eng_score_category',
       'max_eng_score_category', 'sum_eng_score_category','engagement_score']
plt.figure(figsize=(20,10))
sns.heatmap(df_train[new_features].corr(),fmt='.2f',annot=True);
heat map | Engaging videos

Source: Author

We have a high number of features correlation table is not clearly visible but we can still see the colour and by the colour, we can see the newly engineered features are highly correlated with target ‘engagement_score’.

Let’s just focus on the correlation with engagement_score only.

df_corr=(df_train[new_features].corr()['engagement_score']).reset_index()
df_corr.columns=['features','engagement_score']
df_corr.sort_values('engagement_score',ascending=False)

Source: Author

I have plotted the score in descending order and we can see that new features that we have created using engagement_score are highly correlated to it. Features that we have created using ‘views’ and ‘followers’ do not show much correlation to the ‘engagement_score’.

Let’s handle the Categorical features now.

Label Encoding 'gender'
gender_dict={'Male':0,'Female':1}
df_train['gender']=df_train['gender'].map(gender_dict)
df_test['gender']=df_test['gender'].map(gender_dict)

Label Encoding ‘profession’

profession_dict={'Student':0,'Working Professional':1,'Other':2}
df_train['profession']=df_train['profession'].map(profession_dict)
df_test['profession']=df_test['profession'].map(profession_dict)

Let’s try building the models now.

Model Building

We will be building ‘LinearRegression’, ‘XGBoostRegressor’, ‘RandomForestRegressor’ and ‘CatBoostRegressor’ models to solve this problem.

First, let’s select the features that we will be using.

features_to_use=['user_id', 'category_id', 'video_id', 'age', 'gender', 'profession',
       'followers', 'views', 'min_followers',
       'avg_followers', 'max_followers', 'sum_followers', 'min_views',
       'mean_views', 'max_views', 'sum_views', 'min_followers_category',
       'avg_followers_category', 'max_followers_category',
       'sum_followers_category', 'min_views_category', 'mean_views_category',
       'max_views_category', 'sum_views_category', 'min_eng_score',
       'avg_eng_score', 'max_eng_score', 'sum_eng_score',
       'min_eng_score_category', 'avg_eng_score_category',
       'max_eng_score_category', 'sum_eng_score_category']
target=['engagement_score']

While splitting the dataset into train and test it is possible that few users do not end up in the test set. Because of this when we did feature engineering and build the new features using ‘user_id’ it may be possible that test_set contains ‘NULL’ values. So we are dropping those rows.

df_test.dropna(inplace=True)

Defining X_train, y_train, X_test and y_test.

X_train,y_train=df_train[features_to_use],df_train[target]
X_test,y_test=df_test[features_to_use],df_test[target]

We will use R2_Score and Root Mean Square Value (RMSE) as performance metrics to measure the performance of the models.

def model_performance(y_test,y_pred):
    r2=round(r2_score(y_test,y_pred),3)
    rmse=round(np.sqrt(mean_squared_error(y_test,y_pred)),3)
    return r2,rmse

Building Models

model_name=['LinearRegression','XGBRegressor','RandomForestRegressor','CatBoostRegressor']
model_object=[LinearRegression(),XGBRegressor(),RandomForestRegressor(),CatBoostRegressor()]
model_r2_result=[]
model_rmse_result=[]
for i,model in enumerate(model_object):
    print('Running ',model_name[i])
    if model_name[i]=='CatBoostRegressor':
        model.fit(X_train,y_train,cat_features=['user_id', 'category_id', 'gender', 'profession'],verbose=0)
    else:
        model.fit(X_train,y_train)
    y_pred=model.predict(X_test)
    y_pred=np.array(y_pred).reshape(-1,1)
    #engagement score liest between 0 and 5. While prediting it is possible that predicted scores are beyond these
    #limits so keeping this in mind I am limiting the predictions in 0 to 5 range.
    accurate_y_pred=limit_predictions(y_pred,0,5)
    model_r2,model_rmse=model_performance(y_test,accurate_y_pred)
    model_r2_result.append(model_r2)
    model_rmse_result.append(model_rmse)

Now let’s see how the models have performed.

Model Performace

results=PrettyTable([‘Performace_Metric’]+model_name)
results.add_row([‘R2_Score’]+model_r2_result)
results.add_row([‘RMSE_Score’]+model_rmse_result)
print(results)
Model Performance | Engaging videos

Source: Author

We can see that almost all the models have performed well on the data. CatboostRegressor got the highest R2 score and XGBRegressor got the highest RMSE_Score.

Code

Here I am presenting the complete code to solve the problem.

#importing Libraries
import pandas as pd
from catboost import CatBoostRegressor
from sklearn.linear_model import LinearRegression
from xgboost import XGBRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import r2_score,mean_squared_error
import numpy as np
from prettytable import PrettyTable
#Loading Data

df=pd.read_csv('train_0OECtn8.csv')

df.drop('row_id',axis=1,inplace=True)

#EDA

num_features=['age','followers','views']

cat_features=['user_id','category_id','video_id','gender','profession']

df[num_features].hist(figsize=(10,10));

sns.catplot(data=df[cat_features],kind='count',x='profession');

sns.catplot(data=df[cat_features],kind='count',x='gender');

##correlation

plt.figure(figsize=(20,10))

sns.heatmap(df.corr(),fmt='g',annot=True)

##pair plots

sns.pairplot(df)

#Feature Engineering

features=['user_id', 'category_id', 'video_id', 'age', 'gender', 'profession',

'followers', 'views']

target=['engagement_score']

x_train,x_test,y_train,y_test=train_test_split(df[features],df[target],test_size=0.2,random_state=42)

df_train=pd.concat([x_train,y_train],axis=1)

df_test=pd.concat([x_test,y_test],axis=1)

##feautures using views and followers

grp_by=['user_id']

user_id_features=df_train.groupby(grp_by)['followers'].min().reset_index()

user_id_features.rename(columns={'followers':'min_followers'},inplace=True)

user_id_features['avg_followers']=df_train.groupby(grp_by)['followers'].mean().reset_index()['followers']

user_id_features['max_followers']=df_train.groupby(grp_by)['followers'].max().reset_index()['followers']

user_id_features['sum_followers']=df_train.groupby(grp_by)['followers'].sum().reset_index()['followers']

user_id_features['min_views']=df_train.groupby(grp_by)['views'].min().reset_index()['views']

user_id_features['mean_views']=df_train.groupby(grp_by)['views'].mean().reset_index()['views']

user_id_features['max_views']=df_train.groupby(grp_by)['views'].max().reset_index()['views']

user_id_features['sum_views']=df_train.groupby(grp_by)['views'].sum().reset_index()['views']

grp_by1=['user_id','category_id']

user_id_features1=df_train.groupby(grp_by1)['followers'].min().reset_index()

user_id_features1.rename(columns={'followers':'min_followers_category'},inplace=True)

user_id_features1['avg_followers_category']=df_train.groupby(grp_by1)['followers'].mean().reset_index()['followers']

user_id_features1['max_followers_category']=df_train.groupby(grp_by1)['followers'].max().reset_index()['followers']

user_id_features1['sum_followers_category']=df_train.groupby(grp_by1)['followers'].sum().reset_index()['followers']

user_id_features1['min_views_category']=df_train.groupby(grp_by1)['views'].min().reset_index()['views']

user_id_features1['mean_views_category']=df_train.groupby(grp_by1)['views'].mean().reset_index()['views']

user_id_features1['max_views_category']=df_train.groupby(grp_by1)['views'].max().reset_index()['views']

user_id_features1['sum_views_category']=df_train.groupby(grp_by1)['views'].sum().reset_index()['views']

df_train=pd.merge(df_train,user_id_features,how='left',on=grp_by)

df_test=pd.merge(df_test,user_id_features,how='left',on=grp_by)

df_train=pd.merge(df_train,user_id_features1,how='left',on=grp_by1)

df_test=pd.merge(df_test,user_id_features1,how='left',on=grp_by1)

##features using engagement_score

grp_by=['user_id']

user_id_features2=df_train.groupby(grp_by)['engagement_score'].min().reset_index()

user_id_features2.rename(columns={'engagement_score':'min_eng_score'},inplace=True)

user_id_features2['avg_eng_score']=df_train.groupby(grp_by)['engagement_score'].mean().reset_index()['engagement_score']

user_id_features2['max_eng_score']=df_train.groupby(grp_by)['engagement_score'].max().reset_index()['engagement_score']

user_id_features2['sum_eng_score']=df_train.groupby(grp_by)['engagement_score'].sum().reset_index()['engagement_score']

grp_by1=['user_id','category_id']

user_id_features3=df_train.groupby(grp_by1)['engagement_score'].min().reset_index()

user_id_features3.rename(columns={'engagement_score':'min_eng_score_category'},inplace=True)

user_id_features3['avg_eng_score_category']=df_train.groupby(grp_by1)['engagement_score'].mean().reset_index()['engagement_score']

user_id_features3['max_eng_score_category']=df_train.groupby(grp_by1)['engagement_score'].max().reset_index()['engagement_score']

user_id_features3['sum_eng_score_category']=df_train.groupby(grp_by1)['engagement_score'].sum().reset_index()['engagement_score']

df_train=pd.merge(df_train,user_id_features2,how='left',on=grp_by)

df_test=pd.merge(df_test,user_id_features2,how='left',on=grp_by)

df_train=pd.merge(df_train,user_id_features3,how='left',on=grp_by1)

df_test=pd.merge(df_test,user_id_features3,how='left',on=grp_by1)

##correlation using new features

new_features=['min_followers',

'avg_followers', 'max_followers', 'sum_followers', 'min_views',

'mean_views', 'max_views', 'sum_views', 'min_followers_category',

'avg_followers_category', 'max_followers_category',

'sum_followers_category', 'min_views_category', 'mean_views_category',

'max_views_category', 'sum_views_category', 'min_eng_score',

'avg_eng_score', 'max_eng_score', 'sum_eng_score',

'min_eng_score_category', 'avg_eng_score_category',

'max_eng_score_category', 'sum_eng_score_category','engagement_score']

plt.figure(figsize=(20,10))

sns.heatmap(df_train[new_features].corr(),fmt='.2f',annot=True,);

##correlation only with 'engagement_score'

df_corr=(df_train[new_features].corr()['engagement_score']).reset_index()

df_corr.columns=['features','engagement_score']

df_corr.sort_values('engagement_score',ascending=False)

##Handling Categorical Data

gender_dict={'Male':0,'Female':1}

df_train['gender']=df_train['gender'].map(gender_dict)

df_test['gender']=df_test['gender'].map(gender_dict)

profession_dict={'Student':0,'Working Professional':1,'Other':2}

df_train['profession']=df_train['profession'].map(profession_dict)

df_test['profession']=df_test['profession'].map(profession_dict)

#Model Building

features_to_use=['user_id', 'category_id', 'video_id', 'age', 'gender', 'profession',

'followers', 'views', 'min_followers',

'avg_followers', 'max_followers', 'sum_followers', 'min_views',

'mean_views', 'max_views', 'sum_views', 'min_followers_category',

'avg_followers_category', 'max_followers_category',

'sum_followers_category', 'min_views_category', 'mean_views_category',

'max_views_category', 'sum_views_category', 'min_eng_score',

'avg_eng_score', 'max_eng_score', 'sum_eng_score',

'min_eng_score_category', 'avg_eng_score_category',

'max_eng_score_category', 'sum_eng_score_category']

target=['engagement_score']

#since we have a user in the data. It is possible that after splitting the dataset few users do not end up

#in test data after feature engineering. So for simplicity we are dropping null values from test data.

df_test.dropna(inplace=True)

X_train,y_train=df_train[features_to_use],df_train[target]

X_test,y_test=df_test[features_to_use],df_test[target]

def model_performance(y_test,y_pred):

r2=round(r2_score(y_test,y_pred),3)

rmse=round(np.sqrt(mean_squared_error(y_test,y_pred)),3)

return r2,rmse

def limit_predictions(y_pred,min_limit,max_limit):

new_y_pred=[]

for val in y_pred:

val=val[0] #fetching value from array

if val<=min_limit:

val=min_limit

elif val>=max_limit:

val=max_limit

new_y_pred.append(val)

return new_y_pred

Conclusion

In this article, we solved the problem of predicting how engaging a video can be at the user level.

Dataset used was from a ‘Job-a-thon’ contest organized in analytics vidya and was only available to members who have participated in the contest.

We saw that CatboostRegressor got the highest R2_Score and XGBoost got the highest RMSE_score on the data.

Read more blogs on our website.

 

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

p
prshntkmr112

Hi, I am Prashant Kumar Anuragi. I am currently working as Data Scientist in Tiger Analytics, Hyderabad.

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