final-ml-project

This repository contains my Final Machine Learning Project notebook. The project demonstrates the full machine learning pipeline, including:

Data Loading & Preprocessing: Importing datasets, handling missing values, and data normalization.

Exploratory Data Analysis (EDA): Visualizations, feature relationships, and data distribution insights.

Model Building & Training: Implementation of supervised learning algorithms (e.g., regression, classification) with scikit-learn or other frameworks.

Model Evaluation: Performance metrics such as accuracy, precision, recall, or RMSE, plus confusion matrices and ROC curves where relevant.

Hyperparameter Tuning: Use of GridSearchCV or other tuning techniques to optimize model performance.

Insights & Conclusions: Interpretation of results and final remarks on model performance and possible future improvements.

1. Import necessary libraries

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, r2_score from statsmodels.tsa.arima.model import ARIMA

2. Load and preprocess the data

df = pd.read_csv('Rate.csv', parse_dates=['observation_date']) df.rename(columns={'observation_date': 'Date', 'Rate': 'Inflation'}, inplace=True) df.set_index('Date', inplace=True) df = df.asfreq('MS') # monthly start frequency

3. Visualize the original data

plt.figure(figsize=(10, 4)) plt.plot(df['Inflation'], label='Inflation Rate') plt.title('US Inflation Rate (1948 - Present)') plt.xlabel('Year') plt.ylabel('Inflation (%)') plt.legend() plt.grid(True) plt.tight_layout() plt.show()

4. Train/test split (80/20)

split_index = int(len(df) * 0.8) train, test = df.iloc[:split_index], df.iloc[split_index:]

5. ARIMA (AR/ARMA-like univariate model)

Using ARIMA with d=0 for ARMA structure

arima_model = ARIMA(train['Inflation'], order=(2, 0, 1)).fit() arima_forecast = arima_model.forecast(steps=len(test))

6. Create lagged features for Random Forest

def create_lagged_features(series, lags=12): df_lagged = pd.DataFrame() for i in range(1, lags + 1): df_lagged[f'lag_{i}'] = series.shift(i) df_lagged['target'] = series.values return df_lagged.dropna()

lagged_df = create_lagged_features(df['Inflation'], lags=12) X = lagged_df.drop(columns='target') y = lagged_df['target']

X_train = X.iloc[:split_index - 12] X_test = X.iloc[split_index - 12:] y_train = y.iloc[:split_index - 12] y_test = y.ilocsplit_index__