Initial model upload

README.md

@@ -7,7 +7,7 @@ tags:
 - healthcare
 license: mit
 widget:
-- text: "Patient details: Age 65, Hypertension 1, Avg_glucose_level 180, BMI 25"
+- text: "Patient details: Age 45, Hypertension 1, Avg_glucose_level 170, BMI 26"
 datasets:
 - stroke-prediction-dataset
 ---
@@ -36,4 +36,37 @@ weighted avg       0.95      0.95      0.92       982
     ```
 
 ## How to Use
+This model i created in google colab. Relavant libraries include:
+## How to Use
+This runs in google colab.
+
+Import as per below:
+
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import random
+from sklearn.model_selection import GridSearchCV
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+from sklearn.preprocessing import MinMaxScaler
+
+# For kaggle
+import os
+import zipfile
+
+# For Hugging face
+# from sklearn.externals import joblib # to save the model
+from huggingface_hub import notebook_login
+from huggingface_hub import Repository 
+
+
 Download the model and load it using `joblib
+Replace input_data with your  data, e.g.  [[45, 1, 170, 26]]  # Age, Hypertension, Avg_glucose_level, BMI
+
+