This is a Repository containing various projects I carried out while learning different Machine Learning Algorithms.
Problem Statement: Create a chatbot for Tesla company that can answer at least 5 questions related to their electric cars. Use Conditional Statements, Nested Ifs, and Loops in your python code, and be creative when forming your question and answers.
In this Project I used simple if-else statements and nested statements to create chatbot script that takes user inputs and gives out various responses
Problem Statement: Create a function in python, that will print the steps involved in ordering food using a food delivery app. The function should contain one parameter that accepts the name of the food delivery app (Example: Ubereats, etc.).
Only print basic steps in ordering food like the opening app, user login, select restaurant, select food, select delivery location, payment method, etc.
(The first output statement should be Food delivery by (app name passed as an argument ) and then print the steps involved in ordering food.
Also, inside the function ask the user to input the dish and restaurant he wants to order from, then print a statement at the end, specifying the input details as Ordering (dish name) from (restaurant name).
Problem Statement: Using the student_performance dataset, perform basic data exploration, data visualization, and data cleaning as described in your course project.
Plot the histogram for gender, math_score and reading_score (Use boxplot to check for outliers for both math_score and reading_score).
Note: Do not remove the outliers as all the scores are important for further data visualization.
Then remove all other columns from the dataset except 'gender', 'test_preparation_course', 'math_score', 'reading_score' and 'writing_score'.
Now check for any null or nan values in the dataset and perform data cleaning if required.
Add one column to calculate the total score by adding the scores obtained in 'math_score', 'reading_score' and 'writing_score'.
Add another column to calculate the average score by dividing the total score by 3.
Now Perform some data visualization to find which gender has a higher average score, also find the average score of students who completed the test_preparation_course vs the students who did not complete it. (Hint: Use bar plot for both the visualizations).
Using the 'homeprices' dataset, predict prices of new homes based on area, bed rooms and age. Check for missing values and fill in the missing values with the median value of that attribute.
Train the model using linear regression and check the coefficients and intercept value to create the linear equation. Save the model into a .pkl file
Finally predict the price of a home that has,
3000 sqr ft area, 3 bedrooms, 40 years old
2500 sqr ft area, 4 bedrooms, 5 years old
(Cross check the values by manually calculating using the linear equation)
Using the same dataset of fishes used in the class project, predict the width of the fishes using all the other attributes as independent variables.
Check the correlation between the width and other attributes by using heatmap and pairplot. Also, check for outliers using boxplot and remove them if any.
Use 70:30 ratio for training and testing the model then save your model as .pkl file.
Compare the predicted data with the test data and calculate the R2 score to give your conclusions about the accuracy of the model.
Also, predict the width of fishes with the following data:
Weight: 300 vertical:25 diagonal:27 cross:30 height: 8
Weight: 400 vertical:26 diagonal:28 cross:31 height: 9
Weight: 500 vertical:27 diagonal:29 cross:32 height: 10
Problem Statement: From the given 'titanic' dataset use the following columns to build a model to predict if person would survive or not,
Pclass
Gender
Age
Fare
Use label encoder code below to convert the string data in Gender column into numbers (1=male, 0=female).
from sklearn.preprocessing import LabelEncoder
le_gender = LabelEncoder()
df['gender'] = le_gender.fit_transform(df.Gender)
df = df.drop('Gender',axis = 1)
Check for missing or null values and replace them with the mean.
Train your model using train_test_split function with 75-25 ratio. Finally use both decision tree and random forest(n_estimators =400) classifier to predict the x_test data and compare it with the y_test data using confusion matrix.
Give your conclusions about the accuracy of both the classifiers
Calculate the score of your model using the code :
clf.score(X_test,y_test)
Problem Statement: Using the same iris flower dataset, apply direct logistic regression with 70-30 split for training and testing data to predict the species of the flower.
Check the accuracy of the model using confusion matrix and visualize using heatmap.
Code to apply logistic regression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()