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This hackathon was designed to identify the segmentation of future clients of Tesla based on past and current clients. This was created utilizing the Scikit-Learn machine learning models. You can access their Github or their website for more ML methods or documentation.

Installation

Use the package manager pip to install the following packages.

pip install seaborn 
pip install scikit-learn 
pip install pandas

Usage

# Make sure to import all of the following libraries to ensure functionality
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
  

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Contact

Dante Mathieu Schrantz

Miguel Diaz Perez de Juan

Contributing

Contributions to improve this tool are welcome. Feel free to fork this repository and submit your pull requests.

License

MIT License

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