Machine Learning with Python

𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗖𝗔) 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗼𝘀𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗪𝗵𝗮𝘁 𝗘𝘅𝗮𝗰𝘁𝗹𝘆 𝗜𝘀 𝗣𝗖𝗔? ⤷ 𝗣𝗖𝗔 is a 𝗺𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 used to transform a 𝗵𝗶𝗴𝗵-𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 dataset into fewer dimensions, while retaining as much 𝘃𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻) as possible. ⤷ Think of it as “𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗻𝗴” data, similar to how we reduce the size of an image without losing too much detail. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗣𝗖𝗔 𝗶𝗻 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀? ⤷ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 your data for 𝗲𝗮𝘀𝗶𝗲𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 and 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 ⤷ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲 machine learning models by reducing 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝘀𝘁 ⤷ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 multi-dimensional data in 2𝗗 or 3𝗗 for insights ⤷ 𝗙𝗶𝗹𝘁𝗲𝗿 𝗼𝘂𝘁 𝗻𝗼𝗶𝘀𝗲 and uncover hidden patterns in your data 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 ⤷ The 𝗳𝗶𝗿𝘀𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 is the direction in which the data varies the most. ⤷ Each subsequent component represents the 𝗻𝗲𝘅𝘁 𝗵𝗶𝗴𝗵𝗲𝘀𝘁 𝗿𝗮𝘁𝗲 of variance, but is 𝗼𝗿𝘁𝗵𝗼𝗴𝗼𝗻𝗮𝗹 (𝘂𝗻𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱) to the previous one. ⤷ The challenge is selecting how many components to keep based on the 𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲 they explain. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 1: 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Imagine you’re working on a project to 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 customers for a marketing campaign, with data on spending habits, age, income, and location. ⤷ Using 𝗣𝗖𝗔, you can reduce these four variables into just 𝘁𝘄𝗼 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 that retain 90% of the variance. ⤷ These two new components can then be used for 𝗸-𝗺𝗲𝗮𝗻𝘀 clustering to identify distinct customer groups without dealing with the complexity of all the original variables. 𝗧𝗵𝗲 𝗣𝗖𝗔 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 — 𝗦𝘁𝗲𝗽-𝗕𝘆-𝗦𝘁𝗲𝗽 ⤷ 𝗦𝘁𝗲𝗽 𝟭: 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Ensure your data is on the same scale (e.g., mean = 0, variance = 1). ⤷ 𝗦𝘁𝗲𝗽 𝟮: 𝗖𝗼𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗠𝗮𝘁𝗿𝗶𝘅 Calculate how features are correlated. ⤷ 𝗦𝘁𝗲𝗽 𝟯: 𝗘𝗶𝗴𝗲𝗻 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 Compute the eigenvectors and eigenvalues to determine the principal components. ⤷ 𝗦𝘁𝗲𝗽 𝟰: 𝗦𝗲𝗹𝗲𝗰𝘁 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 Choose the top-k components based on the explained variance ratio. ⤷ 𝗦𝘁𝗲𝗽 𝟱: 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Transform your data onto the new 𝗣𝗖𝗔 space with fewer dimensions. 𝗪𝗵𝗲𝗻 𝗡𝗼𝘁 𝘁𝗼 𝗨𝘀𝗲 𝗣𝗖𝗔 ⤷ 𝗣𝗖𝗔 is not suitable when the dataset contains 𝗻𝗼𝗻-𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 or 𝗵𝗶𝗴𝗵𝗹𝘆 𝘀𝗸𝗲𝘄𝗲𝗱 𝗱𝗮𝘁𝗮. ⤷ For non-linear data, consider 𝗧-𝗦𝗡𝗘 or 𝗮𝘂𝘁𝗼𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 instead. https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 📱

𝐊-𝐌𝐞𝐚𝐧𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 - 𝐟𝐨𝐫 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐊-𝐌𝐞𝐚𝐧𝐬? It’s an unsupervised machine learning algorithm that automatically groups your data into K similar clusters without labels. It finds hidden patterns using distance-based similarity. 𝐈𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞: You run a mall. Your data has: › Age › Annual Income › Spending Score K-Means can divide customers into: ⤷ Budget Shoppers ⤷ Mid-Range Customers ⤷ High-End Spenders 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: ① Choose the number of clusters K ② Randomly initialize K centroids ③ Assign each point to its nearest centroid ④ Move centroids to the mean of their assigned points ⑤ Repeat until centroids don’t move (convergence) 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Minimize the total squared distance between data points and their cluster centroids 𝐉 = Σ‖𝐱ᵢ - μⱼ‖² Where 𝐱ᵢ = data point, μⱼ = cluster center 𝐇𝐨𝐰 𝐭𝐨 𝐩𝐢𝐜𝐤 𝐊: Use the Elbow Method ⤷ Plot K vs. total within-cluster variance ⤷ The “elbow” in the curve = ideal number of clusters 𝐂𝐨𝐝𝐞 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 (𝐒𝐜𝐢𝐤𝐢𝐭-𝐋𝐞𝐚𝐫𝐧):

from sklearn.cluster import KMeans
X = [[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]
model = KMeans(n_clusters=2, random_state=0)
model.fit(X)
print(model.labels_)
print(model.cluster_centers_)

𝐁𝐞𝐬𝐭 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: ⤷ Customer segmentation ⤷ Image compression ⤷ Market analysis ⤷ Social network analysis 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬: › Sensitive to outliers › Requires you to predefine K › Works best with spherical clusters https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 📱

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