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57 lines
2.0 KiB
57 lines
2.0 KiB
import streamlit as st
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.linear_model import LogisticRegression
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn import datasets
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from sklearn.impute import SimpleImputer # Add this line
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import pandas as pd
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import numpy as np
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st.header("Prediction")
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if "data" in st.session_state:
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data = st.session_state.data
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with st.form("my_form"):
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header = st.columns([2,1,2])
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header[0].subheader("Model")
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header[1].subheader("Data Name")
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row1 = st.columns([2,1,2])
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model = row1[0].selectbox("", ["Random Forest Classifier", "Random Forest Regressor", "Logistic Regression", "Linear Regression"])
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data_name = row1[1].selectbox("", data.columns)
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st.form_submit_button('launch')
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if model == "Random Forest Classifier":
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model = RandomForestClassifier()
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elif model == "Random Forest Regressor":
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model = RandomForestRegressor()
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elif model == "Logistic Regression":
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model = LogisticRegression()
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elif model == "Linear Regression":
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model = LinearRegression()
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x = data.drop(data_name, axis=1)
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y = data[data_name]
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# Convert categorical data to numerical values
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x = pd.get_dummies(x)
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# Handle missing values
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imputer = SimpleImputer()
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x = imputer.fit_transform(x)
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x = pd.get_dummies(x)
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
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model.fit(x_train, y_train)
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y_pred = model.predict(x_test)
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if model == "Random Forest Classifier":
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st.write("Accuracy: ", accuracy_score(y_test, y_pred))
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elif model == "Random Forest Regressor" or model == "Logistic Regression" or model == "Linear Regression":
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st.write("Mean Squared Error: ", np.mean((y_pred - y_test) ** 2))
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else:
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st.error("file not loaded")
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