ITC Analytics Accelerator

Workshop

• Forecast vs Prediction

Code

• import pandas as pd
• var = pd.read_csv('used_cars_maruti.csv')
• var.describe() | statistics of the data
• var.info() | summary
• var.shape | Rows and columns
• var[0:5] | range of rows to be shown. Minus 5 is also possible
• var['Column Title'][0:5] | First 5 rows are shown
• var'Column Title','Column Title'[0:5]
• var.sample(3) | Random sampling of any 3

OVERVIEW

5i: 
Ideation: defining
Intelligence: process of prediciton
Inception: model building
Intervention: Developing a segment
Independance: Live


DATA ENGINEERING
collection
Sructuring
Cleaning


ALGORITHUMS
Classification & Regression
Decision Tree
Tree based Algorithums (RF model
Ensemble models


MODEL PERFORMANCE
Model selection

AI & ML
ML
supervised
Unsupervised

DEEP LEARNING
Reinforment learning
CNN

IMPLEMENTATION of ADVANCED ANALTICS
Tracking

CHANGE MANAGEMENT
Piller & Practics

Advanced Analytics

Value of AA

• Evolution: Excel > Storage > Tableu > Alerts > predicting the future > ML >
• Applications:
  • Descriptive (historical. advanced statistics) >
  • Predictive (Future. Uses ML. detection of fraud) >
  • Prescriptive (influence the future. Data driven decision making); Optimization;

Five concepts

Statistical Learning

• Frame of understanding the data based on statistics
• Eg: L.Regression: Used in predictive analytics
• Linear model: regression or classification

Inference vs prediction

• Inference : process of evaluating the relationship of predictor and the response
• Prediction: process of using a model that is yet to happen

Flexibility vs Interpretability of models

• Flexibility: capture a wide range of phenomena (Data fit)
• Interpretability : Design a Pattern to facilitate the decision

Regression vs classification

• Regression : Continuous variable.
• classification: discrete variable

Supervised vs unsupervised learning

• Supervised " Labelled data sets, Training , CNN , Decision tree
• unsupervised : Classification
  • Anomaly detection
  • Find hidden structure
  • Eg: Customer segmentation; Purchase patterns, simplify data, monitoring