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app.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import streamlit as st
+import plotly.figure_factory as ff
+
+st.title('A Simple Profit Prediction 💵')
+st.image('https://assets2.ignimgs.com/2013/11/12/the-hobbit-bilbo-gold-1280jpg-e9649d_160w.jpg?width=1280')
+
+st.write('### Data ')
+df = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/Startups.csv")
+st.dataframe(df)
+st.write('This data is about the R&D spend, Administration cost, Marketing Spend, State of operation, and the historical profit generated by 50 startups.')
+
+
+df_graph = df.groupby('State')['Profit'].sum().reset_index()
+st.write("### Total Profits by State")
+st.bar_chart(df_graph, x="State", y="Profit")
+
+
+numeric_columns = df.select_dtypes(include=['float64', 'int64'])
+corr_matrix = numeric_columns.corr()
+fig = ff.create_annotated_heatmap(z=corr_matrix.values,
+                                  x=numeric_columns.columns.tolist(),
+                                  y=numeric_columns.columns.tolist(),
+                                  annotation_text=corr_matrix.round(2).values)
+fig.update_layout()
+st.write("### Correlation Heatmap")
+st.plotly_chart(fig)
+
+st.write('There is a clear correlation between R&D and Profit.Also, administration sucks.')
+
+#model
+st.write('### Model Training')
+x = df[["R&D Spend", "Administration", "Marketing Spend"]]
+y = df["Profit"]
+x = x.to_numpy()
+y = y.to_numpy()
+y = y.reshape(-1, 1)
+from sklearn.model_selection import train_test_split
+xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)
+from sklearn.linear_model import LinearRegression
+model = LinearRegression()
+model.fit(xtrain, ytrain)
+st.write(f"I after train test split, I have defined a LinerRegression Model. It's r2 score is. {model.score(xtest, ytest)}, meaning 90% accuracy")
+
+st.write('### Using It')
+st.write('Change the expenditures to see the difference in profit.')
+# Input boxes for features
+feature1 = st.number_input('R&D Spend', value=100000)
+feature2 = st.number_input('Administration Spend', value=100000)
+feature3 = st.number_input('Marketing Spend', value=100000)
+
+features = np.array([[feature1, feature2, feature3]])
+
+# Assuming 'model' is your trained model
+prediction = model.predict(features)
+st.write(f'Predicted Profit: {prediction[0]}')
+
+st.write('### Conclusion')
+st.write('This model abides the expenditure/profit relation of the 50 startups in this dataset. If you are one of them, this app will be useful. Otherwise, to make a more general model, we simply need more data.')

requirements.txt

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+pandas
+numpy
+scikit-learn
+plotly