Machine Learning Project 1: Honda Motor Stocks best Prices analysis

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Introduction

Are you interested in the fascinating world of Machine Learning and how it can impact financial decisions? Join us on this inaugural project where we will be using Machine Learning to analyze Honda Motor (HMC) stock prices.

Also, check Machine Learning projects:

• Machine Learning Project 1: Honda Motor Stocks best Prices analysis
• Machine Learning Project 2: Diversity in Tech Companies Best EDA Analysis
• Machine Learning Project 3: Exploring Indian Cuisine Best Analysis
• Machine Learning Project 4: Exploring Video Game Data
• Machine Learning Project 5: Best Students Performance EDA
• Machine Learning Project 6: Obesity type Best EDA and classification

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This blog series will be your guide as we embark on this journey. Here’s what you can look forward to:

• Data Delving: We will begin by gathering historical Honda stock data and preparing it for our Machine Learning model.
• Algorithmic Adventure: Get ready to explore various Machine Learning algorithms that are best suited for analyzing stock prices.
• Model Masterclass: Witness the creation and training of our very own Machine Learning model to predict Honda’s stock prices.
• Performance Probe: We will thoroughly evaluate the effectiveness of our model and interpret the results.
• Investment Insights: (Disclaimer: Not financial advice!) We will discuss the potential implications of our findings for understanding Honda’s stock market movements.

So, fasten your seatbelts and prepare to unlock the potential of Machine Learning in the dynamic world of stock analysis! Stay tuned as we embark on this exciting exploration together!

Step 1: Machine Learning Analysis Code

Import Libraries

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
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
import warnings
warnings.filterwarnings('ignore')

Import dataset

Database link: https://www.kaggle.com/datasets/innocentmfa/honda-motor-stocks-prices

# Load the dataset
file_path = 'HMC.csv'
hmc_data = pd.read_csv(file_path)

Step 2: Exploratory Data Analysis (EDA)

The analysis we will perform first is categorized as Exploratory Data Analysis (EDA). EDA is a critical step in the data science process, involving summarizing the key characteristics of a dataset, often using visual methods.

The objectives of EDA are to:

• Understand the underlying structure of the data.
• Identify important variables and their relationships.
• Detect outliers and anomalies.
• Test underlying assumptions.
• Generate hypotheses for further analysis.

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Types of Analysis Conducted

Trend Analysis:

• Observes how stock prices have evolved over time.
• Recognizes general patterns and trends in the data.

Volatility and Anomaly Detection:

• Pinpoints significant spikes or drops in stock prices.
• Aids in understanding periods of high volatility and potential causes.

Volume Analysis:

• Analyzes the trading volume over time.
• Offers insights into trading activity and liquidity.

Distribution Analysis:

• Investigates the distribution of stock prices.
• Aids in understanding the central tendency, spread, and shape of the price data.

Correlation Analysis:

• Explores the relationship between different price columns.
• Assists in understanding how different variables are interconnected.

Importance of EDA:

• Data Quality: Helps in identifying data quality issues like missing values, outliers, and anomalies.
• Hypothesis Generation: Lays the groundwork for generating hypotheses for further analysis and modeling.
• Model Preparation: Guides the feature engineering and selection process for constructing predictive models.

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In order to analyze the past and present events in our dataset, we will need to work with the “Date” column. To make it easier for us to analyze, we should convert the “Date” column into a ‘datetime object’:

# Convert the 'Date' column to a datetime object
hmc_data['Date'] = pd.to_datetime(hmc_data['Date'])

# Sort the data by date
hmc_data = hmc_data.sort_values('Date')

Question 1: What are the general trends in the stock prices over the given period?

# Plot the general trends in the stock prices over time
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['Close'], label='Close Price', color='b')
plt.plot(hmc_data['Date'], hmc_data['Open'], label='Open Price', color='g')
plt.plot(hmc_data['Date'], hmc_data['High'], label='High Price', color='r')
plt.plot(hmc_data['Date'], hmc_data['Low'], label='Low Price', color='c')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Honda Motor Co. (HMC) Stock Prices Over Time')
plt.legend()
plt.show()

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Question 2: Are there any significant spikes or drops in the stock prices? What might have caused these?

# Identify significant spikes or drops in the stock prices
threshold = 0.1  # 10% change threshold for significant changes
hmc_data['Price_Change'] = hmc_data['Close'].pct_change()
significant_changes = hmc_data[np.abs(hmc_data['Price_Change']) > threshold]

# Display the dates with significant changes
significant_changes[['Date', 'Close', 'Price_Change']]

Question 3: How does the trading volume change over time?

# Plot the trading volume over time
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['Volume'], label='Volume', color='m')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.title('Honda Motor Co. (HMC) Trading Volume Over Time')
plt.legend()
plt.show()

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Question 4: What is the distribution of the stock prices?

# Distribution of stock prices
plt.figure(figsize=(14, 7))
sns.histplot(hmc_data['Close'], bins=50, kde=True)
plt.title('Distribution of Close Prices')
plt.xlabel('Close Price')
plt.ylabel('Frequency')
plt.show()

Question 5: What is the correlation between different price columns (Open, High, Low, Close, Adj Close)?

# Correlation matrix
plt.figure(figsize=(10, 8))
correlation_matrix = hmc_data[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Matrix')
plt.show()

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Step 3: Feature Engineering

What is Feature Engineering?

Feature Engineering is a crucial step in machine learning where domain knowledge is applied to generate input variables (features) that enhance the predictive capabilities of algorithms.

This involves various techniques such as creating new features from existing data, transforming existing features to capture underlying patterns more effectively, and combining features to create new ones that better represent the problem being solved.

The quality and quantity of these features have a significant impact on the performance of the machine learning model.

Question 1: Can we create useful technical indicators from the data?

# Create Moving Averages
hmc_data['MA_10'] = hmc_data['Close'].rolling(window=10).mean()
hmc_data['MA_50'] = hmc_data['Close'].rolling(window=50).mean()

# Create Relative Strength Index (RSI)
def compute_rsi(data, window=14):
    delta = data.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

hmc_data['RSI'] = compute_rsi(hmc_data['Close'])

# Create Bollinger Bands
def compute_bollinger_bands(data, window=20):
    sma = data.rolling(window=window).mean()
    std = data.rolling(window=window).std()
    upper_band = sma + (std * 2)
    lower_band = sma - (std * 2)
    return upper_band, lower_band

hmc_data['BB_upper'], hmc_data['BB_lower'] = compute_bollinger_bands(hmc_data['Close'])

# Plotting the technical indicators
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['Close'], label='Close Price')
plt.plot(hmc_data['Date'], hmc_data['MA_10'], label='10-Day MA')
plt.plot(hmc_data['Date'], hmc_data['MA_50'], label='50-Day MA')
plt.plot(hmc_data['Date'], hmc_data['BB_upper'], label='Bollinger Upper Band', linestyle='--')
plt.plot(hmc_data['Date'], hmc_data['BB_lower'], label='Bollinger Lower Band', linestyle='--')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Technical Indicators')
plt.legend()
plt.show()

# Plotting the RSI
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['RSI'], label='RSI', color='purple')
plt.axhline(70, linestyle='--', alpha=0.5, color='r')
plt.axhline(30, linestyle='--', alpha=0.5, color='g')
plt.xlabel('Date')
plt.ylabel('RSI')
plt.title('Relative Strength Index (RSI)')
plt.legend()
plt.show()

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Question 2: Can lagged features be useful predictors for the stock prices?

import pandas as pd

# Load the dataset
file_path = 'HMC.csv'
hmc_data = pd.read_csv(file_path)

# Convert the 'Date' column to a datetime object
hmc_data['Date'] = pd.to_datetime(hmc_data['Date'])

# Sort the data by date
hmc_data = hmc_data.sort_values('Date')

# Create lagged features
hmc_data['Lag_1'] = hmc_data['Close'].shift(1)
hmc_data['Lag_5'] = hmc_data['Close'].shift(5)
hmc_data['Lag_10'] = hmc_data['Close'].shift(10)

# Display the new features
print("Lagged Features:")
print(hmc_data[['Date', 'Close', 'Lag_1', 'Lag_5', 'Lag_10']].head(15))

Question 3: How can we use rolling statistics for analysis?

# Convert the 'Date' column to a datetime object
hmc_data['Date'] = pd.to_datetime(hmc_data['Date'])

# Sort the data by date
hmc_data = hmc_data.sort_values('Date')

# Calculate rolling mean and standard deviation
hmc_data['Rolling_Mean_20'] = hmc_data['Close'].rolling(window=20).mean()
hmc_data['Rolling_Std_20'] = hmc_data['Close'].rolling(window=20).std()

# Plotting rolling statistics
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['Close'], label='Close Price')
plt.plot(hmc_data['Date'], hmc_data['Rolling_Mean_20'], label='20-Day Rolling Mean')
plt.fill_between(hmc_data['Date'], 
                 hmc_data['Rolling_Mean_20'] - 2 * hmc_data['Rolling_Std_20'], 
                 hmc_data['Rolling_Mean_20'] + 2 * hmc_data['Rolling_Std_20'], 
                 color='orange', alpha=0.2, label='2 Std Dev')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Rolling Mean and Standard Deviation')
plt.legend()
plt.show()

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Full Analysis Code

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
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
import warnings
warnings.filterwarnings('ignore')

# Load the dataset
file_path = 'HMC.csv'
hmc_data = pd.read_csv(file_path)

# Convert the 'Date' column to a datetime object
hmc_data['Date'] = pd.to_datetime(hmc_data['Date'])

# Sort the data by date
hmc_data = hmc_data.sort_values('Date')

# Plot the stock prices over time
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['Close'], label='Close Price', color='b')
plt.plot(hmc_data['Date'], hmc_data['Open'], label='Open Price', color='g')
plt.plot(hmc_data['Date'], hmc_data['High'], label='High Price', color='r')
plt.plot(hmc_data['Date'], hmc_data['Low'], label='Low Price', color='c')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Honda Motor Co. (HMC) Stock Prices Over Time')
plt.legend()
plt.show()

# Plot the trading volume over time
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'], hmc_data['Volume'], label='Volume', color='m')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.title('Honda Motor Co. (HMC) Trading Volume Over Time')
plt.legend()
plt.show()

# Distribution of stock prices
plt.figure(figsize=(14, 7))
sns.histplot(hmc_data['Close'], bins=50, kde=True)
plt.title('Distribution of Close Prices')
plt.show()

# Correlation matrix
plt.figure(figsize=(10, 8))
correlation_matrix = hmc_data[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Matrix')
plt.show()

# Feature Engineering
# Create technical indicators (moving averages)
hmc_data['MA_10'] = hmc_data['Close'].rolling(window=10).mean()
hmc_data['MA_50'] = hmc_data['Close'].rolling(window=50).mean()

# Lag features
hmc_data['Lag_1'] = hmc_data['Close'].shift(1)
hmc_data['Lag_5'] = hmc_data['Close'].shift(5)
hmc_data['Lag_10'] = hmc_data['Close'].shift(10)

# Drop rows with NaN values created by rolling window and shift operations
hmc_data = hmc_data.dropna()

# Prepare the data for modeling
features = ['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'Lag_1', 'Lag_5', 'Lag_10']
X = hmc_data[features]
y = hmc_data['Close']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

# Standardize the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train a Linear Regression model
lr_model = LinearRegression()
lr_model.fit(X_train_scaled, y_train)
lr_predictions = lr_model.predict(X_test_scaled)

# Train a Random Forest Regressor model
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
rf_predictions = rf_model.predict(X_test)

# Evaluate the models
lr_mae = mean_absolute_error(y_test, lr_predictions)
lr_rmse = np.sqrt(mean_squared_error(y_test, lr_predictions))

rf_mae = mean_absolute_error(y_test, rf_predictions)
rf_rmse = np.sqrt(mean_squared_error(y_test, rf_predictions))

print(f"Linear Regression MAE: {lr_mae:.4f}")
print(f"Linear Regression RMSE: {lr_rmse:.4f}")
print(f"Random Forest MAE: {rf_mae:.4f}")
print(f"Random Forest RMSE: {rf_rmse:.4f}")

# Plot the true vs predicted prices for Random Forest
plt.figure(figsize=(14, 7))
plt.plot(hmc_data['Date'].iloc[-len(y_test):], y_test, label='True Prices', color='b')
plt.plot(hmc_data['Date'].iloc[-len(y_test):], rf_predictions, label='Predicted Prices (RF)', color='r')
plt.xlabel('Date')
plt.ylabel('Close Price')
plt.title('True vs Predicted Close Prices (Random Forest)')
plt.legend()
plt.show()

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Is Honda a good stock to buy right now?

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Which project is best in machine learning?

Ultimately, the most suitable project for you depends on your interests, expertise, and the specific problem you want to solve.

However, here are some project ideas in machine learning that are often considered impactful and interesting:

• Image Classification: Create a deep learning model that can classify images into different categories, such as identifying objects in photos or detecting diseases in medical images.
• Natural Language Processing (NLP): Develop models for tasks like sentiment analysis, named entity recognition, or text summarization using techniques like recurrent neural networks (RNNs) or transformer models like BERT.
• Recommendation Systems: Build personalized recommendation systems for products, movies, or music based on user preferences and historical data, using collaborative filtering or content-based approaches.
• Generative Adversarial Networks (GANs): Explore the field of generative modeling by building GANs to generate realistic images, create deepfakes, or even generate synthetic data for training other machine learning models.
• Time Series Forecasting: Utilize machine learning algorithms to forecast future values of time series data, such as stock prices, energy demand, or weather patterns, using techniques like ARIMA, LSTM, or attention-based models.
• Anomaly Detection: Develop models to detect unusual patterns or outliers in data, which can be applied to fraud detection, network security, or predictive maintenance in industrial settings.
• Reinforcement Learning: Implement reinforcement learning algorithms to train agents that learn to make decisions by interacting with an environment, such as playing games, optimizing resource allocation, or controlling autonomous vehicles.
• Healthcare Applications: Explore machine learning applications in healthcare, such as predicting patient outcomes, diagnosing diseases from medical images, or personalizing treatment plans based on patient data.

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How to do a ML project?

Here’s a simple example of a machine learning project using Python and the popular scikit-learn library for building a basic linear regression model:

# Step 1: Import necessary libraries
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Step 2: Prepare data
# Generate some sample data for demonstration
np.random.seed(0)
X = 2 * np.random.rand(100, 1)  # Generate 100 random numbers between 0 and 2
y = 4 + 3 * X + np.random.randn(100, 1)  # y = 4 + 3*X + noise

# Step 3: Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 4: Create and train the model
model = LinearRegression()  # Create a linear regression model
model.fit(X_train, y_train)  # Train the model using the training data

# Step 5: Make predictions
y_pred = model.predict(X_test)  # Use the trained model to make predictions on the test data

# Step 6: Evaluate the model
mse = mean_squared_error(y_test, y_pred)  # Calculate mean squared error
print("Mean Squared Error:", mse)

It’s important to note that this is a basic example. Real-world machine learning projects often involve additional steps such as feature engineering, hyperparameter tuning, cross-validation, and deployment.

Here are the fundamental steps of a machine learning project:

• Importing Libraries: Start by importing the necessary libraries such as NumPy for numerical computations, scikit-learn for machine learning algorithms, and any others that may be required.
• Preparing Data: Generate or load the dataset and preprocess it as needed. This may involve handling missing values, scaling features, or any other necessary data transformations.
• Splitting Data: Divide the dataset into training and testing sets. This allows us to evaluate the performance of the model on unseen data.
• Creating and Training the Model: Choose a machine learning algorithm, like linear regression in this case, and train it using the training data. This step involves fitting the model to the training data.
• Making Predictions: Once the model is trained, use it to make predictions on the test data. This helps us understand how well the model generalizes to new, unseen data.
• Evaluating the Model: Assess the performance of the model using appropriate evaluation metrics. For regression problems, mean squared error is a commonly used metric.

What are the examples of ML and Python projects?

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What can I make with machine learning?

Machine learning has a wide range of applications across different domains. Here are some examples of what you can achieve with machine learning:

• Predictive Models: Create models that can predict outcomes such as sales forecasts, customer churn, stock prices, or medical diagnoses.
• Recommendation Systems: Build recommendation engines that suggest products, movies, music, or content based on user preferences and historical data.
• Image Recognition: Develop systems that can identify objects, faces, or patterns in images. This is useful for applications like automated surveillance, medical imaging, or self-driving cars.
• Natural Language Processing (NLP): Design algorithms that process and understand human language. This enables tasks like sentiment analysis, language translation, or chatbots.
• Anomaly Detection: Construct models that can detect unusual patterns or outliers in data. This is valuable for fraud detection, network security, or predictive maintenance.
• Generative Models: Explore generative modeling techniques like Generative Adversarial Networks (GANs) to generate realistic images, text, or music.
• Reinforcement Learning: Implement algorithms that enable agents to learn optimal behaviors through trial and error. This is suitable for applications like game playing, robotics, or autonomous systems.
• Healthcare Applications: Apply machine learning to healthcare problems such as disease diagnosis, personalized treatment recommendation, or medical image analysis.
• Time Series Forecasting: Develop models that can predict future values of time series data. This is useful for tasks like stock market prediction, demand forecasting, or weather forecasting.
• Customer Segmentation: Segment customers based on their behavior and characteristics to tailor marketing strategies, improve customer retention, or personalize product offerings.
• Text Generation: Build models capable of generating human-like text, which can be used for tasks like text summarization or content generation.

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Conclusion

We’ve come to the final chapter of Machine Learning Project 1, where we’ve used the power of Machine Learning to explore Honda Motor’s stock prices. We’ve analyzed the data, tested algorithms, created and trained a model, and assessed its performance.

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Key Takeaways:

• (Summarize the main discoveries from the project. Did the model detect any patterns? What were the constraints?)

Looking Ahead:

Machine Learning provides an intriguing perspective on financial markets. Nevertheless, it’s important to recognize that the stock market is inherently intricate, and our model is just one instrument in the investment toolkit. The future is uncertain, but armed with the insights gained from this project, you’re now better prepared to navigate the ever-changing realm of finance.

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This is only the start! Machine Learning has vast potential for a variety of financial uses. Keep an eye out for upcoming projects where we’ll venture into new areas and address different financial obstacles using Machine Learning.

In the meantime, please share your thoughts in the comments below! What intrigued you the most about this project? Do you have any suggestions for future Machine Learning projects in finance? Let’s continue the discussion!