How-NOT-to-loose-a-customer-ML-Challenge

Problem

Churn rate is a marketing metric that describes the number of customers who leave a business over a specific time period. . Every user is assigned a prediction value that estimates their state of churn at any given time. This value is based on:

User demographic information

Browsing behavior Historical purchase data among other information It factors in our unique and proprietary predictions of how long a user will remain a customer. This score is updated every day for all users who have a minimum of one conversion. The values assigned are between 1 and 5.

Task

Your task is to predict the churn score for a website based on the features provided in the dataset.

Data description

The dataset folder contains the following files:

train.csv: 36992 x 25
test.csv: 19919 x 24
sample_submission.csv: 5 x 2

score = 100 x metrics.f1_score(actual, predicted, average="macro")

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.ipynb_checkpoints		.ipynb_checkpoints
dataset		dataset
output		output
warehouse		warehouse
.ipynb		.ipynb
1-2-Date-Fixed-Random-forest-classifier.ipynb		1-2-Date-Fixed-Random-forest-classifier.ipynb
1-3-Yes-No-Ref-and-Date-Fixed-Random-forest-classifier.ipynb		1-3-Yes-No-Ref-and-Date-Fixed-Random-forest-classifier.ipynb
1-4-All-Value-Fix-Date-Fixed-Random-forest-classifier.ipynb		1-4-All-Value-Fix-Date-Fixed-Random-forest-classifier.ipynb
1-5-Different-Models.ipynb		1-5-Different-Models.ipynb
1-6-Try-Dropping-the-Columns-one-by-one.ipynb		1-6-Try-Dropping-the-Columns-one-by-one.ipynb
1.Final-Accuracy-Quest.ipynb		1.Final-Accuracy-Quest.ipynb
Clean_Data_generator.ipynb		Clean_Data_generator.ipynb
Conclusions from Explolatory Analysis.ipynb		Conclusions from Explolatory Analysis.ipynb
Decision Tree Classifier.ipynb		Decision Tree Classifier.ipynb
Explolatory-data-analysis.ipynb		Explolatory-data-analysis.ipynb
README.md		README.md
Random-forest-classifier-with-cleaning.ipynb		Random-forest-classifier-with-cleaning.ipynb
Random-forest-classifier.ipynb		Random-forest-classifier.ipynb
Reading and Understanding the Data.ipynb		Reading and Understanding the Data.ipynb
Simplest Model-Copy1.ipynb		Simplest Model-Copy1.ipynb
Super_Clean_No_NA_dataset.ipynb		Super_Clean_No_NA_dataset.ipynb
Test_data_understanding.ipynb		Test_data_understanding.ipynb
Visualisations using Seaborn.ipynb		Visualisations using Seaborn.ipynb
how_NOT_to_lose_customer.zip		how_NOT_to_lose_customer.zip
try-2-Negative Values Dropped-Copy1.ipynb		try-2-Negative Values Dropped-Copy1.ipynb
try-3-ID Columns Dropped-Copy1.ipynb		try-3-ID Columns Dropped-Copy1.ipynb
try-4-Clean Data LR model.ipynb		try-4-Clean Data LR model.ipynb

Mayank-MP05/How-NOT-to-loose-a-customer-ML-Challenge

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How-NOT-to-loose-a-customer-ML-Challenge

Problem

User demographic information

Task

Data description

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