Python Machine Learning

Introduction

In this lab, you will be transported to a futuristic underwater city where you take on the role of a deep-sea treasure hunter. Your mission is to use Python machine learning skills to analyze underwater data and locate hidden treasures. The underwater city is filled with mysterious artifacts and valuable treasures waiting to be discovered. Your goal is to utilize your Python machine learning knowledge to uncover the secrets of the ocean depths.

Skills Graph

Data Preprocessing and Exploration

In this step, you will begin by loading and preprocessing the underwater data collected from various sensors. You will explore the dataset to gain insights into the underwater environment and identify patterns using Python's pandas and numpy libraries.

In ~/project/data_preprocessing.py:

## data_preprocessing.py

import pandas as pd
import numpy as np

## Load the underwater dataset into a pandas DataFrame
underwater_data = pd.read_csv('/home/labex/project/underwater_data.csv')

## Perform data preprocessing and exploration
print(underwater_data)

Run the script:

python data_preprocessing.py

The information below should be displayed on your terminal:

   sensor1  sensor2  sensor3  sensor4
0      1.2      3.4      5.6      2.1
1      2.3      4.5      6.7      3.2
2      3.4      5.6      7.8      4.3

Model Training and Evaluation

In this step, you will utilize Python's scikit-learn library to build machine learning models for predicting the locations of potential underwater treasures based on the preprocessed data. You will train and evaluate the performance of various machine learning algorithms such as decision trees, random forests, and support vector machines.

In ~/project/model_training.py:

## model_training.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

## Read the data from the "underwater_data.csv" file
data = pd.read_csv("/home/labex/project/underwater_data.csv")

## Convert data to a NumPy array
data = np.array(data)

## Extract feature matrix X and target variable y
X = data[:, :-1]  ## Use all rows, except the last column as the feature matrix X
y = data[:, -1]   ## Use all rows, the last column as the target variable y

## Split the preprocessed 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)

## Initialize and train a random forest regressor
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

## Evaluate the model's performance
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")

Run the script:

python model_training.py

The information below should be displayed on your terminal:

Mean Squared Error: 1.8009639999999907

Summary

In this lab, we designed a hands-on experience for aspiring treasure hunters to apply Python machine learning techniques in the futuristic underwater city. By preprocessing and exploring underwater data and training machine learning models, you can uncover hidden treasures and gain valuable insights into the application of machine learning in real-world scenarios.