From b752532042d122591f6f6bf6acc6589febcac99d Mon Sep 17 00:00:00 2001 From: Jake Pullen Date: Mon, 11 May 2026 07:37:50 +0100 Subject: [PATCH] batman --- car_price_guesser/.gitignore | 13 + car_price_guesser/.python-version | 1 + car_price_guesser/README.md | 57 + car_price_guesser/data_analysis_temp.py | 27 + car_price_guesser/predict.py | 147 + car_price_guesser/pyproject.toml | 13 + car_price_guesser/train_model.py | 197 + car_price_guesser/uv.lock | 387 + iris_flowers/.gitignore | 10 + iris_flowers/.python-version | 1 + iris_flowers/README.md | 57 + iris_flowers/agents.md | 10 + iris_flowers/linear_regression.py | 115 + iris_flowers/main.py | 16 + iris_flowers/plots/heatmap.png | Bin 0 -> 39923 bytes iris_flowers/plots/histograms.png | Bin 0 -> 40445 bytes iris_flowers/plots/linear_regression.png | Bin 0 -> 47369 bytes iris_flowers/plots/pairplot.png | Bin 0 -> 260711 bytes iris_flowers/plots/polynomial_regression.png | Bin 0 -> 62869 bytes iris_flowers/polynomial_regression.py | 85 + iris_flowers/pyproject.toml | 12 + iris_flowers/uv.lock | 385 + iris_flowers/visualize.py | 63 + kaggle_machine_learning/.gitignore | 10 + kaggle_machine_learning/.python-version | 1 + kaggle_machine_learning/README.md | 0 kaggle_machine_learning/main.py | 75 + kaggle_machine_learning/melb_data.csv | 13581 +++++++++++++++++ kaggle_machine_learning/melb_data.csv.zip | Bin 0 -> 461423 bytes kaggle_machine_learning/pyproject.toml | 10 + kaggle_machine_learning/train.csv | 1461 ++ kaggle_machine_learning/uv.lock | 182 + 32 files changed, 16916 insertions(+) create mode 100644 car_price_guesser/.gitignore create mode 100644 car_price_guesser/.python-version create mode 100644 car_price_guesser/README.md create mode 100644 car_price_guesser/data_analysis_temp.py create mode 100644 car_price_guesser/predict.py create mode 100644 car_price_guesser/pyproject.toml create mode 100644 car_price_guesser/train_model.py create mode 100644 car_price_guesser/uv.lock create mode 100644 iris_flowers/.gitignore create mode 100644 iris_flowers/.python-version create mode 100644 iris_flowers/README.md create mode 100644 iris_flowers/agents.md create mode 100644 iris_flowers/linear_regression.py create mode 100644 iris_flowers/main.py create mode 100644 iris_flowers/plots/heatmap.png create mode 100644 iris_flowers/plots/histograms.png create mode 100644 iris_flowers/plots/linear_regression.png create mode 100644 iris_flowers/plots/pairplot.png create mode 100644 iris_flowers/plots/polynomial_regression.png create mode 100644 iris_flowers/polynomial_regression.py create mode 100644 iris_flowers/pyproject.toml create mode 100644 iris_flowers/uv.lock create mode 100644 iris_flowers/visualize.py create mode 100644 kaggle_machine_learning/.gitignore create mode 100644 kaggle_machine_learning/.python-version create mode 100644 kaggle_machine_learning/README.md create mode 100644 kaggle_machine_learning/main.py create mode 100644 kaggle_machine_learning/melb_data.csv create mode 100644 kaggle_machine_learning/melb_data.csv.zip create mode 100644 kaggle_machine_learning/pyproject.toml create mode 100644 kaggle_machine_learning/train.csv create mode 100644 kaggle_machine_learning/uv.lock diff --git a/car_price_guesser/.gitignore b/car_price_guesser/.gitignore new file mode 100644 index 0000000..8c41dc9 --- /dev/null +++ b/car_price_guesser/.gitignore @@ -0,0 +1,13 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +*.joblib +*.csv \ No newline at end of file diff --git a/car_price_guesser/.python-version b/car_price_guesser/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/car_price_guesser/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/car_price_guesser/README.md b/car_price_guesser/README.md new file mode 100644 index 0000000..5470ecd --- /dev/null +++ b/car_price_guesser/README.md @@ -0,0 +1,57 @@ +# Car Price Guesser + +## Purpose +This project is a learning exercise for building machine learning models. The goal is to predict the price of a car based on its attributes (e.g., Year, Mileage, Brand, Model, etc.) using a dataset of New York cars. + +## Getting Started + +This project uses `uv` for dependency management. + +### Prerequisites +- Python 3.14+ +- `uv` installed + +### Installation +1. Clone the repository (if applicable). +2. Install dependencies: + ```bash + uv sync + ``` + +## Usage + +### 1. Train the Model +To train the machine learning model (Linear Regression baseline): +```bash +uv run train_model.py +``` +This script will: +- Load the `New_York_cars.csv` dataset. +- Preprocess the data (handle missing values, encode categorical features). +- Train the model. +- Save the trained model to `car_price_model.joblib`. +- Output performance metrics (MSE, R2 Score). + +### 2. Make Predictions +You can use the trained model to guess the price of a car. + +**Interactive Mode:** +```bash +uv run predict.py +``` +Follow the prompts to enter car details. + +**Command Line Arguments:** +```bash +uv run predict.py +``` +Example: +```bash +uv run predict.py 2020 50000 Toyota +``` + +**Random Row Verification:** +To pick a random car from the dataset and compare the model's prediction against the actual price: +```bash +uv run predict.py --random +``` diff --git a/car_price_guesser/data_analysis_temp.py b/car_price_guesser/data_analysis_temp.py new file mode 100644 index 0000000..a6a2631 --- /dev/null +++ b/car_price_guesser/data_analysis_temp.py @@ -0,0 +1,27 @@ +import pandas as pd +import numpy as np + +# Load data +file_path = "New_York_cars.csv" +print(f"Loading {file_path}...") +df = pd.read_csv(file_path) + +print("Columns:", df.columns.tolist()) +print("\nShape:", df.shape) + +print("\nSample 'money' values:", df["money"].head(10).tolist()) +print("\nSample 'Mileage' values:", df["Mileage"].head(10).tolist()) + +# Check info +print("\nInfo:") +print(df.info()) + +# Check missing +print("\nMissing values:\n", df.isnull().sum()) + +# Check 'money' column type and issues +# It seems to be int64 based on previous head, but let's verify if there are non-numeric +print("\nMoney description:\n", df["money"].describe()) + +# Check 'Mileage' +print("\nMileage description:\n", df["Mileage"].describe()) diff --git a/car_price_guesser/predict.py b/car_price_guesser/predict.py new file mode 100644 index 0000000..732b6e6 --- /dev/null +++ b/car_price_guesser/predict.py @@ -0,0 +1,147 @@ +import pandas as pd +import joblib +import sys +import random + +# Define features used in training (must match train_model.py) +FEATURES = [ + "Year", + "Mileage", + "brand", + "new&used", + "Exterior color", + "Interior color", + "Drivetrain", + "MPG", + "Fuel type", + "Transmission", + "Engine", + "Convenience", + "Entertainment", + "Exterior", + "Safety", + "Seating", + "Accidents or damage", + "Clean title", + "1-owner vehicle", + "Personal use only", + "Model", +] + + +def predict_random_row(): + try: + model = joblib.load("car_price_model.joblib") + except FileNotFoundError: + print( + "Error: Model file 'car_price_model.joblib' not found. Please run train_model.py first." + ) + return + + print("Loading data to pick a random row...") + try: + df = pd.read_csv("New_York_cars.csv") + except FileNotFoundError: + print("Error: 'New_York_cars.csv' not found.") + return + + # Pick a random row + random_index = random.randint(0, len(df) - 1) + row = df.iloc[[random_index]] + + print(f"\nSelected Row Index: {random_index}") + print("-" * 30) + + # Display selected features + for feature in FEATURES: + val = row[feature].values[0] + print(f"{feature}: {val}") + + actual_price = row["money"].values[0] + print("-" * 30) + print(f"Actual Price: ${actual_price:,.2f}") + + # Predict + try: + # Ensure we only pass the features the model expects + input_data = row[FEATURES] + prediction = model.predict(input_data)[0] + print(f"Predicted Price: ${prediction:,.2f}") + + diff = prediction - actual_price + percent_diff = (diff / actual_price) * 100 + print(f"Difference: ${diff:,.2f} ({percent_diff:+.2f}%)") + + except Exception as e: + print(f"Error during prediction: {e}") + + +def predict_price(year, mileage, brand): + # NOTE: This manual function is now limited compared to the full model. + # The model now expects many more features. + # For now, we will warn the user or try to fill others with defaults/unknowns if possible, + # but realistically, manual entry of 20+ features is hard. + # We will just try to predict with what we have and let the model pipeline handle missing cols if it can, + # or error out. + + print( + "Warning: The model now uses many features. Manual entry only supports Year, Mileage, Brand." + ) + print( + "Other features will be set to default/unknown values, which may affect accuracy." + ) + + try: + model = joblib.load("car_price_model.joblib") + except FileNotFoundError: + print("Error: Model file 'car_price_model.joblib' not found.") + return + + # Create dataframe with all expected features initialized to NaN or appropriate defaults + input_data = pd.DataFrame(columns=FEATURES) + input_data.loc[0] = [None] * len(FEATURES) # Initialize with None + + input_data["Year"] = year + input_data["Mileage"] = mileage + input_data["brand"] = brand + + # Fill others if necessary (the pipeline handles NaNs for some, but let's see) + # The training pipeline uses SimpleImputer, so NaNs should be handled. + + # Predict + try: + prediction = model.predict(input_data)[0] + print(f"\nEstimated Price for {year} {brand} with {mileage} miles:") + print(f"${prediction:,.2f}") + except Exception as e: + print(f"Error during prediction: {e}") + + +def main(): + print("--- Car Price Guesser ---") + + if len(sys.argv) > 1 and sys.argv[1] == "--random": + predict_random_row() + return + + if len(sys.argv) == 4: + year = int(sys.argv[1]) + mileage = float(sys.argv[2]) + brand = sys.argv[3] + predict_price(year, mileage, brand) + else: + print("Options:") + print("1. Random Row Verification: uv run predict.py --random") + print("2. Manual Entry: uv run predict.py ") + print("\nEntering interactive manual mode...") + try: + year = int(input("Year (e.g., 2020): ")) + mileage = float(input("Mileage (e.g., 50000): ")) + brand = input("Brand (e.g., Toyota): ") + predict_price(year, mileage, brand) + except ValueError: + print("Invalid input. Please enter numbers for Year and Mileage.") + + +if __name__ == "__main__": + main() diff --git a/car_price_guesser/pyproject.toml b/car_price_guesser/pyproject.toml new file mode 100644 index 0000000..031db74 --- /dev/null +++ b/car_price_guesser/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "car-price-guesser" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.14" +dependencies = [ + "matplotlib>=3.10.7", + "numpy>=2.3.5", + "pandas>=2.3.3", + "scikit-learn>=1.7.2", + "seaborn>=0.13.2", +] diff --git a/car_price_guesser/train_model.py b/car_price_guesser/train_model.py new file mode 100644 index 0000000..2e27474 --- /dev/null +++ b/car_price_guesser/train_model.py @@ -0,0 +1,197 @@ +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression +from sklearn.ensemble import RandomForestRegressor +from sklearn.metrics import mean_squared_error, r2_score +from sklearn.preprocessing import OneHotEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from sklearn.impute import SimpleImputer +import joblib + + +def load_and_prepare_data(): + """ + Load the dataset and prepare it for modeling. + + Returns: + tuple: X (features), y (target) + """ + print("Loading data...") + df = pd.read_csv("New_York_cars.csv") + + # Remove rows where target 'money' is missing + df = df.dropna(subset=["money"]) + + # Define features and target + features = [ + "Year", + "Mileage", + "brand", + "new&used", + "Exterior color", + "Interior color", + "Drivetrain", + "MPG", + "Fuel type", + "Transmission", + "Engine", + "Convenience", + "Entertainment", + "Exterior", + "Safety", + "Seating", + "Accidents or damage", + "Clean title", + "1-owner vehicle", + "Personal use only", + "Model", + ] + target = "money" + + X = df[features] + y = df[target] + + return X, y + + +def create_preprocessor(): + """ + Create a preprocessing pipeline for handling both numeric and categorical features. + + Returns: + ColumnTransformer: Preprocessing pipeline + """ + # Define numeric and categorical features + numeric_features = ["Year", "Mileage"] + categorical_features = [ + "brand", + "MPG", + "Fuel type", + "Transmission", + "Engine", + "Safety", + "Accidents or damage", + "Clean title", + "1-owner vehicle", + "Personal use only", + "Model", + ] + + # Numeric preprocessing pipeline + numeric_transformer = Pipeline( + steps=[("imputer", SimpleImputer(strategy="median"))] + ) + + # Categorical preprocessing pipeline + categorical_transformer = Pipeline( + steps=[ + ("imputer", SimpleImputer(strategy="constant", fill_value="Unknown")), + ("onehot", OneHotEncoder(handle_unknown="ignore", sparse_output=False)), + ] + ) + + # Combine preprocessors + preprocessor = ColumnTransformer( + transformers=[ + ("num", numeric_transformer, numeric_features), + ("cat", categorical_transformer, categorical_features), + ] + ) + + return preprocessor + + +def train_model(X_train, y_train, preprocessor): + """ + Train a linear regression model with preprocessing. + + Args: + X_train: Training features + y_train: Training target + preprocessor: Preprocessing pipeline + + Returns: + Pipeline: Trained model pipeline + """ + print("Training Linear Regression...") + + # Create model pipeline + lr_pipeline = Pipeline( + steps=[("preprocessor", preprocessor), ("regressor", LinearRegression())] + ) + + # Fit the model + lr_pipeline.fit(X_train, y_train) + + return lr_pipeline + + +def evaluate_model(model, X_test, y_test): + """ + Evaluate the trained model using MSE and R2 metrics. + + Args: + model: Trained model pipeline + X_test: Test features + y_test: Test target + + Returns: + tuple: (mse, r2) + """ + # Make predictions + y_pred = model.predict(X_test) + + # Calculate metrics + mse = mean_squared_error(y_test, y_pred) + rmse = np.sqrt(mse) + r2 = r2_score(y_test, y_pred) + + 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--git a/iris_flowers/.gitignore b/iris_flowers/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/iris_flowers/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/iris_flowers/.python-version b/iris_flowers/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/iris_flowers/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/iris_flowers/README.md b/iris_flowers/README.md new file mode 100644 index 0000000..5b2926c --- /dev/null +++ b/iris_flowers/README.md @@ -0,0 +1,57 @@ +# Iris Flowers Learning Space + +## Goal +This repository serves as a personal learning sandbox for exploring data science concepts, machine learning algorithms, and Python data tools using the classic Iris dataset. The primary objective is to move beyond simple "hello world" examples and dive into the thought processes behind data analysis. + +## Exploration & Findings + +We started by visualizing the dataset to understand the underlying structure of the data. Using `visualize.py`, we generated several key plots to inform our modeling strategy. + +### 1. Feature Distributions (Histograms) +**Observation**: The histograms show that different species have distinct distributions for certain features. notably, **Petal Length** shows a clear bimodal distribution, hinting that one species is significantly smaller than the others, while the other two are closer in size. + +### 2. Feature Correlations (Heatmap) +**Observation**: There is a very strong positive correlation between **Petal Length** and **Petal Width** (correlation coefficient > 0.9). +**Data Science Perspective**: +- **Multicollinearity**: This indicates that these two features carry very similar information. In statistical models like Linear Regression, this can make coefficient estimates unstable. +- **Dimensionality Reduction**: This strong correlation suggests that techniques like **PCA (Principal Component Analysis)** would be highly effective here. We could likely compress these two features into a single principal component without losing significant information. + +### 3. Pairwise Relationships (Pairplot) +**Observation**: +- **Iris-setosa** is linearly separable from the other two species. It forms a tight, distinct cluster, especially when looking at petal dimensions. +- **Iris-versicolor** and **Iris-virginica** show some overlap, particularly in sepal dimensions, but are reasonably distinguishable using petal dimensions. +**Data Science Perspective**: +- **Model Selection**: Since one class is easily separable and the others have some overlap, even simple linear classifiers (like Logistic Regression or Linear SVM) should achieve high accuracy (likely > 95%). +- **Complexity**: To perfectly separate Versicolor and Virginica, a model might need a non-linear decision boundary (like a Kernel SVM or Decision Tree), but the risk of overfitting to the small overlap area should be considered. + +## Regression Analysis + +We investigated the relationship between **Petal Length** and **Petal Width** using regression models. + +### Linear Regression +We fitted a simple linear model ($y = mx + c$). +- **Result**: The model achieved an $R^2$ score of **0.9283**, confirming the strong linear relationship observed in the correlation heatmap. +- **Interpretation**: Petal width increases proportionally with petal length. + +### Polynomial Regression +We tested if a "curvy" line would fit better by introducing polynomial terms (degrees 2 and 3). +- **Degree 2 (Quadratic)**: $R^2 = 0.9283$. No improvement over the linear model. +- **Degree 3 (Cubic)**: $R^2 = 0.9376$. A slight improvement, capturing some non-linearity at the extreme values of the data. +- **Conclusion**: While the cubic model is technically better, the added complexity yields only marginal gains. For most practical purposes, the simple linear model is sufficient and more interpretable. + +## Usage + +To reproduce the visualizations and see these patterns for yourself: + +```bash +uv run python visualize.py +``` + +To run the regression models: + +```bash +uv run python linear_regression.py +uv run python polynomial_regression.py +``` + +This will generate the plots in the `plots/` directory. diff --git a/iris_flowers/agents.md b/iris_flowers/agents.md new file mode 100644 index 0000000..1e23012 --- /dev/null +++ b/iris_flowers/agents.md @@ -0,0 +1,10 @@ +# Agent Guidelines + +This file contains instructions and preferences for AI agents working on this repository. + +## Environment and Package Management + +- **Tool**: Use `uv` for all environment and package management tasks. +- **Running Scripts**: Use `uv run