weekly project update

Author: lucifertrj

Diff for: Day7: EDA_cont/Untitled.ipynb

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+Streamlit Web App to Predict Medical Charges
+
+A Practice program to test you knowledge to apply different Regression algorthims other than Linear Regression. s
+
+Install Required Packages
+
+```py
+pip install -r requirements.txt
+```
+
+Run the Code
+
+```py
+streamlit run app.py
+```
+
+Tutorial: [Build Machine Learning App Using Streamlit](https://animevyuh.org/build-machine-learning-model-using-streamlit/)
+
+In [100DaysOfML](https://github.com/lucifertrj/100DaysOfML), we shall work on Streamlit library for 2 days, after we complete Clustering. 

Diff for: Machine Learning Projects/Video_Game_Sale_EDA/VideoGameSales.ipynb

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sb
+import plotly.express as px
+
+st.title("Predict Medical Charges")
+st.header("Machine Learning: Regression")
+st.write("Provided Medical Charges dataset, we shall predict price using Regression ML Algorithms")
+
+st.sidebar.markdown("[Anime Vyuh](https://animevyuh.org/)")
+st.sidebar.markdown("[Check Tutorial](https://animevyuh.org/build-machine-learning-model-using-streamlit/)")
+
+st.header("Analyse The Data")
+data = pd.read_csv('medical.csv')
+st.write("100 Sample Data:")
+st.dataframe(data.sample(100))
+
+st.markdown("### Description Of Data")
+st.write(data.describe())
+
+st.markdown("### Check for Empty Data")
+st.write(data.isnull().sum())
+
+st.markdown("### Correlation Of Features Based on Label(charges)")
+st.write(data.corr()['charges'].sort_values())
+st.write("Here age,bmi, children are numeric feature")
+st.write("We shall later encode the category(object) data type")
+
+st.header("Visualize The Data")
+
+fig = px.histogram(data,marginal='box',x="age",color="smoker")
+fig.update_layout(bargap=0.3)
+st.plotly_chart(fig)
+
+fig,axes = plt.subplots()
+sb.heatmap(data.corr(),annot=True)
+st.pyplot(fig)
+
+fig2,axes2 = plt.subplots()
+plt.pie(data['region'].value_counts(),labels=['southwest','southeast','northwest','northeast'],autopct="%0.2f%%",colors=["purple","blue",'green','red'],explode=[0.01,0.02,0,0])
+st.pyplot(fig2)
+
+from sklearn.ensemble import AdaBoostRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error,mean_squared_error
+
+st.header("Prediction Of Our Model")
+features = ['age','bmi','smoker']
+x = data[features]
+
+category = list()
+numerical = list()
+for features in x.columns:
+    if x[features].dtype == 'O':
+        category.append(features)
+    else:
+        numerical.append(features)
+
+scale = MinMaxScaler()
+x[numerical] = scale.fit_transform(x[numerical])
+
+encode = LabelEncoder()
+x[category] = encode.fit_transform(x[category])
+
+x = x[numerical+category]
+y = data['charges']
+
+st.markdown("### After scaling, encoding and Feature Selection")
+st.markdown("#### Features")
+st.dataframe(x)
+
+st.markdown("#### Label")
+st.dataframe(y)
+
+x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)
+
+st.markdown("#### Change Learning Rate And Notice the Error And Score")
+lr = st.slider("Learning rate",min_value=0.03,max_value=0.1,step=0.01)
+model = AdaBoostRegressor(n_estimators=1000,random_state=42,learning_rate=lr)
+model = model.fit(x_train,y_train)
+predict = model.predict(x_test)
+
+st.write(f"MAE: {mean_absolute_error(predict,y_test)}")
+st.write(f"RMSE: {mean_squared_error(predict,y_test,squared=False)}")
+st.write(f"Score:{model.score(x_train,y_train)*100}%")
+
+st.markdown("<h1 style='text-align: center; color: red;'>Thank You</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: center; color:blue;'><a href='https://www.buymeacoffee.com/trjtarun'>Contribute</a></h3>", unsafe_allow_html=True)