G79-6 Data Analysis and Machine Learning

DataFrame creation from a dictionary. Viewing columns, data types, and summary statistics. Calculating mean, min, and max values. Filtering data using conditions, isin(), and str.contains(). Sorting data by a column. Adding new columns.

Creating a DataFrame with salary, department, and experience. Selecting columns (single and multiple). Accessing rows using .loc[] by labels. Accessing specific cells for targeted information. Using .iloc[] for integer-based indexing. Filtering rows based on conditions (e.g., employees with experience ≥ 5 years).

Creating a DataFrame with track name, genre, duration, and danceability. Calculating average values, like the average song duration. Finding the most common category using .mode(). Counting occurrences of each genre with .value_counts(). Plotting data using pandas’ built-in plotting capabilities (kind="bar").

Creating a DataFrame with days of the week and lemonade sales. Plotting a line chart to show sales trends over the week. Customizing the chart with titles, axis labels, markers, and grid lines for better readability.

Creating a DataFrame with screen time and mood rating data. Plotting a scatter chart to visualize how screen time might affect mood. Customizing the chart with titles, axis labels, grid lines, and marker colors for better clarity.

Creating a DataFrame with ice cream flavors and satisfaction ratings. Grouping data by a categorical column (Flavor) and calculating the average satisfaction. Plotting a bar chart to compare average satisfaction across flavors. Customizing the chart with titles, axis labels, colors, and y-axis limits for clarity.

Loading a dataset (mpg) and handling missing values. Computing correlations between numerical columns (mpg, horsepower, weight) to identify relationships. Visualizing relationships with a scatter plot (weight vs mpg) to observe how car weight affects fuel efficiency. Adding titles to improve plot readability.

** ### What is Machine Learning? ** Machine Learning is a way to teach computers to learn from data — without explicitly programming them what to do. Instead of writing rules, you give the machine examples, and it figures out the patterns on its own. Machine Learning (ML) is a branch of Artificial Intelligence (AI) that works on algorithm developments and statistical models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. ### Machine Learning Is Like a Smart Decision-Maker** Once we teach a computer using data, we want it to make predictions. These predictions fall into two major types: Classification and Regression.(Types of supervised learning) ### Classification vs Regression ``` Classification = Sorting “Is it this or that?” → Is this tumor cancerous? → Is this email spam? Regression = Estimating “How much or how many?” → What will the temperature be tomorrow? → How many marks will the student score? ```

``` Classification:- Predict categories (e.g., Pass/Fail, 0/1) Algorithms used:- Linear Regression, Decision Trees (regressor), RandomForestRegressor Regression:- Predict continuous values (e.g., scores, prices, temperatures) Algorithms used: -Logistic Regression, SVM, Decision Trees (classifier) ```

A Movie Rating Recommender is a fun and relatable project that introduces students to recommendation systems — especially content-based filtering using machine learning and pandas. Let’s build a menu-driven Movie Recommender that: Suggests movies based on genres or user preferences Shows average ratings Uses logistic regression to predict whether a user will like a movie (like/dislike)

Prompt the user to input sepal and petal measurements, and predict the species using the trained model.

Student will present a project