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8 Commits
knn
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csv-delimi
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1
.gitignore
vendored
1
.gitignore
vendored
@@ -1 +1,2 @@
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__pycache__
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__pycache__
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.venv
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@@ -1,5 +1,6 @@
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import pandas as pd
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import pandas as pd
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import streamlit as st
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import streamlit as st
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import codecs
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st.set_page_config(
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st.set_page_config(
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page_title="Project Miner",
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page_title="Project Miner",
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@@ -9,10 +10,13 @@ st.set_page_config(
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st.title("Home")
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st.title("Home")
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### Exploration
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### Exploration
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uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
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uploaded_file = st.file_uploader("Upload your CSV file", type=["csv", "tsv"])
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separator = st.selectbox("Separator", [",", ";", "\\t"])
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separator = codecs.getdecoder("unicode_escape")(separator)[0]
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has_header = st.checkbox("Has header", value=True)
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if uploaded_file is not None:
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if uploaded_file is not None:
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st.session_state.data = pd.read_csv(uploaded_file)
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st.session_state.data = pd.read_csv(uploaded_file, sep=separator, header=0 if has_header else 1)
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st.session_state.original_data = st.session_state.data
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st.session_state.original_data = st.session_state.data
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st.success("File loaded successfully!")
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st.success("File loaded successfully!")
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64
frontend/pages/prediction_classification.py
Normal file
64
frontend/pages/prediction_classification.py
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@@ -0,0 +1,64 @@
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import streamlit as st
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from sklearn.linear_model import LogisticRegression
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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.preprocessing import LabelEncoder
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import pandas as pd
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st.header("Prediction: Classification")
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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("classification_form"):
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st.subheader("Classification Parameters")
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data_name = st.multiselect("Features", data.columns)
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target_name = st.selectbox("Target", data.columns)
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test_size = st.slider("Test Size", min_value=0.1, max_value=0.5, value=0.2, step=0.1)
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st.form_submit_button('Train and Predict')
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if data_name and target_name:
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X = data[data_name]
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y = data[target_name]
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label_encoders = {}
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for column in X.select_dtypes(include=['object']).columns:
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le = LabelEncoder()
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X[column] = le.fit_transform(X[column])
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label_encoders[column] = le
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if y.dtype == 'object':
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le = LabelEncoder()
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y = le.fit_transform(y)
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label_encoders[target_name] = le
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
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model = LogisticRegression()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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st.subheader("Model Accuracy")
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st.write(f"Accuracy on test data: {accuracy:.2f}")
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st.subheader("Enter values for prediction")
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pred_values = []
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for feature in data_name:
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if feature in label_encoders:
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values = list(label_encoders[feature].classes_)
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value = st.selectbox(f"Value for {feature}", values)
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value_encoded = label_encoders[feature].transform([value])[0]
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pred_values.append(value_encoded)
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else:
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value = st.number_input(f"Value for {feature}", value=0.0)
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pred_values.append(value)
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prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
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if target_name in label_encoders:
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prediction = label_encoders[target_name].inverse_transform(prediction)
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st.write("Prediction:", prediction[0])
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else:
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st.error("File not loaded")
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29
frontend/pages/prediction_regression.py
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frontend/pages/prediction_regression.py
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import streamlit as st
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from sklearn.linear_model import LinearRegression
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import pandas as pd
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st.header("Prediction: Regression")
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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("regression_form"):
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st.subheader("Linear Regression Parameters")
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data_name = st.multiselect("Features", data.select_dtypes(include="number").columns)
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target_name = st.selectbox("Target", data.select_dtypes(include="number").columns)
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st.form_submit_button('Train and Predict')
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if data_name and target_name:
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X = data[data_name]
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y = data[target_name]
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model = LinearRegression()
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model.fit(X, y)
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st.subheader("Enter values for prediction")
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pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
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prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
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st.write("Prediction:", prediction[0])
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else:
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st.error("File not loaded")
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