added my solutions

LR3/Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.12.2-alpine
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install -r requirements.txt
+
+EXPOSE 8081
+
+CMD ["streamlit", "run", "regression.py"]

LR3/README.md

@@ -0,0 +1,7 @@
+run the following commands to open app:
+
+docker pull ziyadabd/regression
+
+docker run --name regression-ziyad -p 80:8501 ziyadabd/regression
+
+open http://localhost:80 in browser

LR3/regression.py

@@ -0,0 +1,175 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+from sklearn.metrics import mean_squared_error, r2_score
+
+st.title('Real Estate Price Prediction')
+
+st.markdown("""
+
+1. **Data Cleaning and Structuring**
+2. **Exploratory Data Analysis (EDA)**
+3. **Model Building**
+4. **Model Evaluation**
+5. **Model Deployment**
+""")
+
+train_data = pd.read_csv('Train Real estate.csv')
+test_data = pd.read_csv('Test Real estate.csv')
+
+# Rename the target column in the test data to avoid issues
+if 'Y house price of unit area' in test_data.columns:
+    test_data.rename(columns={'Y house price of unit area': 'Predicted price'}, inplace=True)
+
+st.subheader('1. Data Cleaning and Structuring')
+
+# Display the first few rows of the datasets
+st.markdown("### Initial Dataset Overview")
+st.write('**Training Data:**')
+st.dataframe(train_data.head())
+st.write('**Test Data:**')
+st.dataframe(test_data.head())
+
+# Handling missing values
+st.markdown("### Handling Missing Values")
+st.write("Checking for missing values in the training data:")
+st.write(train_data.isnull().sum())
+st.write("Checking for missing values in the test data:")
+st.write(test_data.isnull().sum())
+
+# Handling missing values - filling with mean as an example
+train_data.fillna(train_data.mean(), inplace=True)
+test_data.fillna(test_data.mean(), inplace=True)
+st.write("Missing values have been filled with the mean of each column.")
+
+# Checking for duplicates
+st.markdown("### Checking for Duplicates")
+st.write(f"Number of duplicate rows in training data: {train_data.duplicated().sum()}")
+st.write(f"Number of duplicate rows in test data: {test_data.duplicated().sum()}")
+
+# Removing duplicates
+train_data.drop_duplicates(inplace=True)
+test_data.drop_duplicates(inplace=True)
+st.write("Duplicates have been removed.")
+
+# Feature engineering (if necessary)
+st.markdown("### Feature Engineering")
+st.write("No additional feature engineering was necessary for this dataset at this stage.")
+
+# Dropping unneeded columns
+st.markdown("### Dropping Unneeded Columns")
+st.write("All columns in the provided datasets are relevant, so none were dropped.")
+
+# Splitting the data into features and target
+X_train = train_data.drop('Y house price of unit area', axis=1)
+y_train = train_data['Y house price of unit area']
+
+# Display the shape of the data
+st.write(f"**Shape of X_train (features):** {X_train.shape}")
+st.write(f"**Shape of y_train (target):** {y_train.shape}")
+
+# 2. Exploratory Data Analysis (EDA)
+st.subheader('2. Exploratory Data Analysis (EDA)')
+
+# Basic statistics
+st.markdown("### Basic Statistics of Features")
+st.write(X_train.describe())
+
+# Distribution of the target variable
+st.markdown("### Distribution of Target Variable (House Price per Unit Area)")
+plt.figure(figsize=(8, 4))
+sns.histplot(y_train, kde=True)
+st.pyplot(plt.gcf())  # Ensures the plot is rendered before continuing
+
+# Pairplot to see relationships between features
+st.markdown("### Pairplot to Explore Relationships Between Features")
+sns.pairplot(train_data)
+st.pyplot(plt.gcf())  # Ensures the plot is rendered before continuing
+
+# Correlation heatmap
+st.markdown("### Correlation Heatmap")
+plt.figure(figsize=(10, 6))
+sns.heatmap(train_data.corr(), annot=True, cmap='coolwarm', fmt='.2f')
+st.pyplot(plt.gcf())  # Ensures the plot is rendered before continuing
+
+# Discussing insights from the heatmap
+st.markdown("""
+- There is a strong negative correlation between house age (`X2 house age`) and house price per unit area (`Y house price of unit area`). This suggests that newer houses tend to have higher prices.
+- The distance to the nearest MRT station (`X3 distance to the nearest MRT station`) is also negatively correlated with the house price, indicating that properties closer to transit are generally more expensive.
+- The number of convenience stores (`X4 number of convenience stores`) shows a moderate positive correlation with house prices, suggesting that properties with more nearby amenities are valued higher.
+""")
+
+# 3. Model Building and Comparison
+st.subheader('3. Model Building and Comparison')
+
+X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
+
+# Scaling the features
+st.markdown("### Scaling the Features")
+st.write("Feature scaling is applied to ensure that the different scales of the features don't negatively impact the regression models.")
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train_split)
+X_val_scaled = scaler.transform(X_val)
+
+# Initialize models
+models = {
+    "Linear Regression": LinearRegression(),
+    "Ridge Regression": Ridge(alpha=1.0),
+    "Lasso Regression": Lasso(alpha=0.1)
+}
+
+# Train models and evaluate them
+st.markdown("### Training and Evaluating Multiple Models")
+
+results = {}
+for model_name, model in models.items():
+    model.fit(X_train_scaled, y_train_split)
+    y_val_pred = model.predict(X_val_scaled)
+
+    mse = mean_squared_error(y_val, y_val_pred)
+    r2 = r2_score(y_val, y_val_pred)
+
+    results[model_name] = {"MSE": mse, "R2": r2}
+
+    st.write(f"**{model_name}:**")
+    st.write(f"Mean Squared Error (MSE): {mse:.2f}")
+    st.write(f"R-squared (R2): {r2:.2f}")
+    st.write("---")
+
+# Comparing models
+st.subheader('4. Model Comparison')
+
+# Results DataFrame
+results_df = pd.DataFrame(results).T
+st.write("### Comparison of Model Performance")
+st.dataframe(results_df)
+
+
+# 5. Conclusion and Model Deployment
+st.subheader('5. Conclusion and Model Deployment')
+
+# Best model selection
+best_model_name = results_df['R2'].idxmax()
+best_model = models[best_model_name]
+
+st.write(f"**The best performing model is: {best_model_name}** with an R-squared value of {results_df.loc[best_model_name, 'R2']:.2f}")
+
+# Drop the target column from the test dataset if it exists
+if 'Predicted price' in test_data.columns:
+    test_features = test_data.drop('Predicted price', axis=1)
+else:
+    test_features = test_data
+
+# Predicting on test data with the best model
+test_data_scaled = scaler.transform(test_features)
+test_predictions = best_model.predict(test_data_scaled)
+
+st.markdown("### Predictions on Test Data with Best Model")
+st.write(test_predictions)
+
+st.write("# PLEASE REFRESH THE PAGE IF SOME PLOTS DO NOT SHOW.")

LR3/requirements.txt

@@ -0,0 +1,7 @@
+streamlit==1.35.0
+pandas==2.2.2
+numpy==1.26.4
+seaborn==0.13.2
+matplotlib==3.9.0
+scikit-learn==1.5.1
+statsmodels==0.14.2

TS3/Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.12.2-alpine
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install -r requirements.txt
+
+EXPOSE 8080
+
+CMD ["streamlit", "run", "time.py"]

TS3/README.md

@@ -0,0 +1,7 @@
+run the following commands to open app:
+
+docker pull ziyadabd/timeseries
+
+docker run --name timeseries-ziyad -p 80:8501 ziyadabd/timeseries
+
+open http://localhost:80

TS3/requirements.txt

@@ -0,0 +1,9 @@
+streamlit==1.35.0
+pandas==2.2.2
+numpy==1.26.4
+seaborn==0.13.2
+matplotlib==3.9.0
+scikit-learn==1.5.1
+statsmodels==0.14.2
+prophet==1.1.5
+pmdarmia==2.0.4