DATA SCIENCEAFFECTLY & FITNESS

INTRODUCTION

1. Data Processing2. Machine Learning3. Model Evaluation4. Visualization & Reporting

• Getting Data• Exploratory Data Analysis• Cleaning Data• Transforming Data~ BI

2. MACHINE LEARNINGChoosing the right:• Problem• Model• Algorithm

• Predictive Analysis• Natural Language Processing• Segmentation• Recommendation...

2. MACHINE LEARNING (MODELS & ALGORITHMS)

• Boosting• AdaBoost• XGBoost• Gradient Boosting...

• Deep Learning• Artificial NN• Convulnational NN• Recurrent NN...

3. MODEL EVALUATION

k-fold cross-validation

AFFECTLY (SEGMENTATION)Problems:• Data • Resource (memory)• Reporting

AFFECTLY (SEGMENTATION)Problems:• Data • Resource (memory)• Reporting

AFFECTLY (PREDICTIVE ANALYSIS)

Problems:• Right model• Data

Top secret

FITNESS (PREDICTIVE ANALYSIS)

pretty simple

FITNESS (RECOMMENDATION)• Popularity – Based• Content – Based• Demographic – Based

(Facebook)• Collaborative Filtering• Hybrid systems

• Problems:• User_data• Item_data

• Approach:• User – User• Item – Item

• Algorithm:• k-NN• Latent Factors (MF)

• Feedback:• Explicit (k-NN)• Implicit (MF)

• Domain:• Single• Multiple

Q&A

Thank you for listening