End-To-End-ML Project

@c17hawke

- Sunny Chandra

Get the Overview

Frame the problem

  1. Define business objective
  2. Select an algorithm based on step 1
  3. Select a performance measure to evaluate the model
  4. how the current solution looks like? if its there. 

Select a Performance Measure

  • In the case of regression tasks-
    • RMSE
    • MAE
  • In case of classification -
    • F score
    • Precision
    • Recall etc

RMSE - Root mean square error

MAE - Mean absolute error

this is considered when there's many outliers present

RMSE -  corresponds to Euclidean norm/distance /     norm  denoted as        or 

MAE -  corresponds to Manhatten norm/distance /     norm  denoted as        

Generally,      norm of a vector v containing n elements is defined as below -

The higher the norm index,

the more it'll focus on large values and neglects the small ones.

that's why RMSE is more sensitive to outliers than MAE and performs well when outliers are rare

Get the Data

Create a workspace

Use conda environments or python virtualenvs

#### conda environments ####
# run this in your terminal or cmd
# this conda command will create an virtual isolated environment 
# by name "my_env" with specified python version 3.6

conda create -n my_env python=3.6

# to activate conda env -
conda activate my_env

# to deactivate conda env -
conda deactivate

#### conda env alternative - virtualenv ####
# mkdir for project and then cd into that dir

# create an env
virtualenv my_env

# activate env -
source my_env/bin/activate # for linux or max

.\my_env\Scripts\activate # for windows

# deactivate env -
deactivate

Download the Data

It is advised to create a function to get the data if it changes frequently

Exploratory Data Analysis

Get the initial glimpse

df.head()

df.info()

df['categorical Val'].value_counts()

df.describe() # for numerical values

df["list of categorical values"].describe()

Example of categorical data description -

Create a test set

# for general large dataset 
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split( df, test_size=0.2, random_state=42 )


# for small dataset and to avoid the risk of sampling bias
# here population is divided into homogeneous subgroups called "strata"
from sklearn.model_selection import StratifiedShuffleSplit

split = StratifiedShuffleSplit( n_splits=1, test_size=0.2, random_state=42 )
for train_index, test_index in split.split(df, df["featureName"]):
    strat_train_set = df.loc[train_index]
    strat_test_set = df.loc[test_index]

Visual investigation

  • Dist plot
  • pair plot
  • joint plot
  • scatter plot
  • box plot

Dist plot

Pair plot

Joint plot

Scatter plot

Box plot

Feature Engineering

  • Try creating new features by combining two different features and check if there exists some correlation or not with the target column

Prepare data to feed in an ML algorithm

Title Text

Subtitle

Select and Train a model

Fine-tune your model

Launch, monitor and maintain  

Reference

End-To-End-ML Project

By Sunny

Made with Slides.com

End-To-End-ML Project

End-To-End-ML Project with reference from chap 2 of Hands on ML book by Aurelien Geron

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