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Gen AI Chat Bot Python Project using Sentence transformer model all-MiniLM-L6-v2

Step 1 – Import libraries needed for text encoding, similarity calculation, and chatbot interface. SentenceTransformer – Converts sentences into numerical vectors so machines can understand the meaning of text. scikit-learn (cosine_similarity) – Calculates similarity between two text vectors to find how closely related they are. NumPy – Used for numerical computing, arrays, matrices, and fast mathematical operations. Pandas – U sed for handling datasets in table format such as reading data, filtering, cleaning, and manipulating rows and columns. ipywidgets – Creates interactive UI elements like text boxes, buttons, sliders, and dropdowns in notebook environments. IPython.display – Displays widgets, HTML, images, and formatted output inside notebooks. Step 2 – Loading the model – SentenceTransformer loads the model "all-MiniLM-L6-v2" which converts sentences into numerical embeddings. Step 3 – Creating product data – A dictionary is created with product names and product de...
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Loop based Math Chat Agent in Python

print ( "🤖 Simple AI Chat Agent" ) print ( "Type 'exit' anytime to stop\n" ) #creating empty list to store the chats later chat_history = [] #creating function for mathematical calculations since its a math chat agent def calculations ( expression ):     try :         result = eval (expression)         return f "The answer is {result} "     except :         return "I couldn’t understand that.please use numbers instead of letters" #creating function for storing message, how and what to respond def chatrespond ( usermessage ):     usrmsg = usermessage.lower()     chat_history.append(usermessage)     # Greetings - when user says hello     if "hello" in usrmsg or "hi" in usrmsg:         return "Hello! How can I help you?"     # when user asks How are you     if "how are you" in usrmsg:         retu...
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Polynomial Regression Python Code - Loan Default Prediction

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Import necessary libraries import pandas import numpy import matplotlib.pyplot from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures Importing data into project data = pandas.read_csv('loan_default_rate_dataset.csv') print(data) Assign Input Columns to X variable and Output column to Y Variable x = data[['Credit_Score','Annual_Income','Loan_Amount']] y = data['Loan_Default_Probability'] Importing Polynomial Features to do cross connections, squares in x data. So data will expand and model can learn unpredictable patterns poly = PolynomialFeatures(degree=2) training the X variable using the polynomial feature. So it will do cross connections, squares in x data and learn the complex patterns. x_poly = poly.fit_transform(x) Now, since we learned the complex patterns and expanded the x values into x_poly variable, we can do linear regression from the Polynomial X data and Y data. model = LinearRegres...
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What is Polynomial Regression? Linear Regression vs Polynomial Regression

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 Polynomial Regression is an Advance method of Linear Regression. Linear Regression draws a line between X and Y data values, we can check how well the relationship between X and Y. Polynomial Regression draws a Curved Line which bends when relationship is unpredictable and non Linear. In a non-linear relationship , the connection between X and Y is not consistent.  Sometimes when X increases , Y increases , Other times when X increases , Y decreases , and it can change direction multiple times . Linear Regression → works best when the relationship between X and Y is proportional and predictable . The line just shows how much Y increases when X increases. Polynomial Regression → works best when the relationship is non-linear or unpredictable . The curve bends up and down showing that sometimes X increases Y, sometimes decreases Y, depending on the data pattern.
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Linear Regression in Python to find Relationship between two columns – Test and Train Explanation

  You have a dataset with Columns "Study Hours" and "Exam Score" with 1000 rows. You split the rows 800 separately and 200 separately. 80% of rows for training the Linear regression model and 20% of rows for testing the Linear regression model. What is Training Data? Training Data is the data we use to teach the model . We give the model Study Hours Column and Exam Scores Column  so it can find the relationship between these columns using the formula Y = M × X + C This is the Python code we use to train the Model:  model.fit(x,y) After training, the model learns: Value of M which is also called as Slope  Value of C which is also called as Intercept  What is Testing Data? Testing Data is new data that the model has never seen before. We give it only Study Hours (X) and ask the model to predict Exam Scores (Y) . Then we compare the Predicted Y with the Real Y to see how well our model works.
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Showing Relationship between X and Y Values using Linear Regression Line in Python - Machine Learning

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Importing Libraries for this project import numpy as np  import pandas as pd from sklearn.linear_ model import LinearRegression  import matplotlib.pyplot as plt  Importing Data file into python code  filepath = 'PythonRegressionPracticeWorkbook.csv'  data = pd.read_csv(filepath)  print(data)  Assigning Input / Independent Column to X and assigning dependent / Output Column to Y variables  x = data[['Square Feet']]  y = data['Price']  Training the X and Y variables using Linear Regression Model to find how X affects Y model = LinearRegression()  model.fit(x,y)  Checking Slope, Intercept values from the learned Model slope = model.coef_[0]  intercept = model.intercept_  print("Slope:", slope)  print("Intercept:",intercept)  Predicting the output value / Y value for each X value / input value  y_pred = model.predict(x)  Plotting Scatter plot with Regression Line to show both Original X and Y values, P...
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Linear Regression Prediction of future values in Python using Scatter Plot - Machine Learning

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We are Importing Necessary Libraries for this project  import pandas as pandas  import numpy as numpy  import matplotlib.pyplot as pyplot  from sklearn.linear_model import LinearRegression Importing Data using Pandas library and creating table dataframe data = pandas.read_csv('python_linear_regression_prediction_python_data.csv')  print(data) Assigning Input / Independent columns to X variable and Assigning Output / Dependent Column to Y Variable x = data[['Experience_Years','Calls_Made','Meetings_Conducted']]  y = data['Sales_Amount'] Asking Linear Regression Model to learn data from X and Y variables model = LinearRegression() model.fit(x,y) Printing Coefficient and Intercept to check their values print("Coefficient:",model.coef_)  print("Intercept",model.intercept_) Adding New Inputs / Independent Values to predict Output / Dependent values new_data = pandas.DataFrame({ 'Experience_Years': [8, 12, 15], '...
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