Build a Neural Network model with python notebook, (tensorflow, keras, scikitln) for preventing customer churn
$10-30 USD
Betalt ved levering
Data science project for Deep learning.
Build a neural network-based classifier that can determine whether the customer will leave or not in the next 6 months.
Data Description
The case study is from an open-source dataset from Kaggle. The dataset contains 10,000 sample points with 14 distinct features such as CustomerId, CreditScore, Geography, Gender, Age, Tenure, Balance, etc.
Best Practices for Notebook :
• The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.
• The notebook should be run from start to finish in a sequential manner before submission.
• It is preferable to remove all warnings and errors before submission.
Criteria Points
Reading Dataset and Feature Elimination
- Read the dataset properly - Print the overview of the data (statistical summary, shape, info, etc) - Eliminate the unique features from the dataset with proper reasoning
Perform an Exploratory Data Analysis on the data
- Checked whether the dataset is balanced or not - Bivariate analysis - Use appropriate visualizations to identify the patterns and insights - Any other exploratory deep dive
Illustrate the insights based on EDA
-Key meaningful observations from Bivariate analysis
Data Pre-processing
- Split the target variable and predictors - Split the data into train and test - Rescale the data
Model building
- Build Neural Network
Model Performance Improvement
-Comment on which metric is right for model performance evaluation and why? - Find the optimal threshold using ROC-AUC or Precision-Recall curves - Comment on model performance - Can model performance be improved? check and comment - Build another model to implement these improvements - Include all the model which were trained to reach at the final one
Model Performance Evaluation
- Evaluate the model on different performance metrics and comment on the performance and scope of improvement
Conclusion and key takeaways
- Final conclusion about the analysis
Notebook overall
- Structure and flow - Well commented code
Projekt ID: #30563284
Om projektet
Tildelt til:
Hi, I am a machine learning engineer and a data analyst and have many experiences in different machine learning methods such as supervised learning, unsupervised learning, reinforcement learning, genetic algorithm, con Flere
17 freelancere byder i gennemsnit $44 timen for dette job
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Hi, I'm interested in this project and glad to help. I've read the requirements and completely understand its content. I'm a data scientist with three years of experience. I'm an expert in data cleaning, data prepro Flere
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