Sure, I'd be happy to help get you started! Here are general steps you can follow to build a basic machine learning model using Python. We will use a widely known sklearn library and build a simple linear regression model. This is a simplified model for educational purposes, intended to introduce you to the fundamentals. Real-world models will include more comprehensive data preparation, feature engineering, and model evaluation techniques. Prerequisites: You should have Python, Scikit-learn, Pandas, Numpy, and Matplotlib libraries installed. Step 1: Loading the Data ```python from sklearn.datasets import load_boston boston = load_boston() ``` Step 2: Understanding the Data ```python import pandas as pd data = pd.DataFrame(boston.data) data.columns = boston.feature_names data['PRICE'] = boston.target ``` In this dataset, ‘PRICE’ is the dependent variable and the remaining are the independent features. Step 3: Splitting the Data ```python from sklearn.model_selection import train_test_split X = data.drop('PRICE', axis = 1) Y = data['PRICE'] X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5) ``` Here we are splitting our data. The variable 'test_size' is where we actually specify the proportion of the test set. Step 4: Building the Model ```python from sklearn.linear_model import LinearRegression lm = LinearRegression() lm.fit(X_train, Y_train) ``` With Scikit-learn you can build the machine learning model in these two lines only. 'fit' method is training the model with the train data. Step 5: Predicting the Data ```python Y_pred = lm.predict(X_test) ``` Above line of code will predict the 'PRICE' for test data. Step 6: Evaluating the Model ```python from sklearn.metrics import mean_absolute_error print(mean_absolute_error(Y_test, Y_pred)) ``` In the end, we use mean absolute error module from sklearn library to get the error rate and understand how well our model performed. Note: Machine learning involves a lot more detail and depth. This is a simplified example to get you started. To deepen your knowledge, consider exploring topics like feature engineering, different types of machine learning algorithms (Decision Trees, Neural Networks etc), tuning parameters, and understanding metrics for model evaluation. Online platforms like Coursera, Udemy, edX provide a wealth of materials on these topics. Books like "The Hundred-Page Machine Learning Book" by Andriy Burkov and "Python Machine Learning" by Sebastian Raschka are also excellent resources.