AI-Driven Market Analysis: Predict & Profit with ML Models

Identify data sources by using internal data, like sales records and website analytics, to tailor models, or external data, like market reports and social media, to build general, adaptable models.

Clean data is essential for reliable modeling; this lecture explains handling missing values by deleting rows or imputing with mean, median, or mode, and using correlation to identify impactful features.

Normalize data to a 0–1 range and standardize to zero mean and unit variance, preserving the ratio between features. Apply these methods in clustering and distance-based analyses.

Practice feature engineering to create new numerical features from categorical data, using dummy variables and hot encoding to vectorize categories such as French, German, and English.

Gain hands-on experience with data collection, cleaning, and pre-processing, including handling missing values, using Python. Encode categoricals, engineer features, and apply normalization to prepare ml-ready datasets for market analysis.

Explore exploratory data analysis to understand data structure, variables, distributions, and relationships, then visualize patterns, clean data, detect outliers, perform descriptive statistics, and inform modeling decisions.

Explore descriptive statistics to summarize data with central tendency, dispersion, and shape, using pandas to compute mean, median, mode, range, variance, standard deviation, interquartile range, skewness, and correlation.

Master data visualization techniques such as histograms, box plots, scatter plots, and correlation matrices to analyze price and property square feet relationships.

Explore supervised and unsupervised learning, and prepare labeled data with features and outputs. Train and evaluate models, then deploy predictive analytics for market analysis.

Explore linear regression on a banking dataset, including data preparation with pandas and train-test split. Compare results with a random forest regressor using r-squared and mean squared error.

Explore unsupervised learning techniques, including clustering and principal component analysis, and learn how k-means groups data by similarity using scikit-learn, with visualization and elbow method to choose clusters.

Explore unsupervised learning with principal component analysis, a dimensionality reduction technique that reduces features to two components while preserving information; standardize data, apply pca, and visualize clusters.

Learn how Kubernetes enables scalable, resource-efficient AI workloads by containerizing apps, supporting GPUs and TPUs, and automating deployment across clouds.

Learn to evaluate AI models with classification and regression metrics, including accuracy, precision, recall, F1, ROC AUC, MSE, MAE, and log loss, focusing on true positives and class balance.

Explore hyperparameters and their tuning to optimize model performance, clarifying how external hyperparameters differ from data-learned parameters and comparing grid search, random search, Bayesian, and gradient-based methods.

Explore overfitting and underfitting in ML models, their causes such as insufficient data or too many features, symptoms like score gaps, and remedies including regularization and cross-validation.

learn to apply data cleaning, pre-processing, and modeling techniques to real-world datasets, focusing on predicting customer churn in a telco scenario using usage, contract, and demographic features.

Prepare data for model training by cleaning, handling missing values, and engineering features; one-hot encode categorical variables, drop noisy or identifier features like customer id, and convert charges to numeric.

Split the dataset into train and test sets with a 0.2 test size and random state 42 using scikit-learn, then train and predict with logistic regression for binary outcomes.

Forecast future sales from the sales forecast dataset with date, totals, store details, promotions, and external factors to guide inventory management, demand planning, and marketing strategy while measuring promotion effectiveness.

Import pandas, load the train data, and clean and pre-process it for analysis. Convert date strings to datetime and extract year, month, and day for feature engineering and future modeling.

Build a predictive model by preparing data, standardizing sales, and applying linear regression with train-test split and grid search for hyperparameter tuning.

Evaluate model performance using mean squared error and R2, explore scaling and feature importance like promo year and holidays, and tune linear regression with grid search and cross-validation.

Explore AI-driven customer segmentation using unsupervised learning and clustering to group customers by behavior and characteristics, enabling tailored marketing strategies and targeted product updates.

Apply k-means clustering with a standard scaler to segment customers by age, annual income, and spending score, using the elbow method to determine four clusters and visualize the results.