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--- |
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tags: |
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- random-forest |
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- stroke-prediction |
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- classification |
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- healthcare |
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license: mit |
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widget: |
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- text: "Patient details: Age 45, Hypertension 1, Avg_glucose_level 170, BMI 26" |
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datasets: |
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- stroke-prediction-dataset |
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--- |
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# Stroke Prediction Model |
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# Date 2024-12-19 |
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This model uses a Random Forest Classifier to predict the likelihood of a stroke based on patient details. |
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## Model Details |
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- **Algorithm**: Random Forest |
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- **Use Case**: Healthcare, Stroke Risk Prediction |
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- **Performance Metrics**: |
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- **Accuracy**: 94.70% |
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- **ROC-AUC Score**: 0.79 |
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- **Classification Report**: |
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``` |
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precision recall f1-score support |
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0 0.95 1.00 0.97 929 |
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1 1.00 0.02 0.04 53 |
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accuracy 0.95 982 |
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macro avg 0.97 0.51 0.50 982 |
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weighted avg 0.95 0.95 0.92 982 |
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``` |
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## How to Use |
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This model i created in google colab. Relavant libraries include: |
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## How to Use |
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This runs in google colab. |
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Import as per below: |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import random |
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from sklearn.model_selection import GridSearchCV |
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from sklearn.preprocessing import StandardScaler, LabelEncoder |
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from sklearn.ensemble import RandomForestClassifier |
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from sklearn.model_selection import train_test_split |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix |
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from sklearn.preprocessing import MinMaxScaler |
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# For kaggle |
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import os |
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import zipfile |
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# For Hugging face |
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# from sklearn.externals import joblib # to save the model |
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from huggingface_hub import notebook_login |
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from huggingface_hub import Repository |
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Download the model and load it using `joblib |
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Replace input_data with your data, e.g. [[45, 1, 170, 26]] # Age, Hypertension, Avg_glucose_level, BMI |
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