The .resample() method in pandas works similarly to .groupby(), but it is specifically designed for time-series data. It groups data into defined time intervals and then applies one or more functions to each group. This method is useful for both upsampling—where missing data points can be filled or interpolated—and downsampling, which involves aggregating data over […]
Python Pandas JSON
JSON (JavaScript Object Notation) is a lightweight, human-readable data interchange format that is widely used for both data storage and transfer. It is structured using key-value pairs and supports various data types, including strings, numbers, booleans, arrays, and nested objects. JSON is a standard format commonly used in APIs and web data, which makes it […]
beautifulsoup pagination
import requests – Allows us to make HTTP requests to web pages. from bs4 import BeautifulSoup –It is used to parse and extract data from HTML content. import pandas as pd – It is used for organizing and manipulating data in table format. import re – It enables pattern matching using regular expressions. from time […]
adaboost classifier
Adaptive Boosting, or AdaBoost, is a boosting algorithm that combines multiple low-accuracy (weak) models to form a single high-accuracy (strong) model. It works by sequentially training these weak learners, each one focusing more on the errors made by the previous ones. Any machine learning algorithm that supports weighted training samples—such as Decision Trees, Logistic Regression, […]
Gradient boosting classifier
Gradient Boosting is an ensemble technique that builds a strong model by combining multiple weak decision trees. While it may seem similar to a Random Forest, there’s a key difference: in Random Forests, each tree is built independently, whereas in Gradient Boosting, trees are built sequentially, with each new tree correcting the errors of the […]
Kaggle House price prediction Regression Analysis
train_df = train_df.drop(columns=[‘PoolQC’, ‘MiscFeature’, ‘Alley’, ‘Fence’, ‘GarageYrBlt’, ‘GarageCond’, ‘BsmtFinType2’]) test_df = test_df.drop(columns=[‘PoolQC’, ‘MiscFeature’, ‘Alley’, ‘Fence’, ‘GarageYrBlt’, ‘GarageCond’, ‘BsmtFinType2’]) #drop GarageArea or GarageCars #build models
kaggle titanic tutorial
#military – Capt, Col, Major #noble – Jonkheer, the Countess, Don, Lady, Sir #unmaried Female – Mlle, Ms, Mme #NEW Drop Sibsp, Parch, TicketNumberCounts #OLD #X = train_df.drop([‘Survived’], axis=1) #y = train_df[‘Survived’] #X_test = test_df.drop([‘Age_Cut’, ‘Fare_Cut’], axis=1)
python variance and standard deviation
https://youtu.be/p4H2b2x_nWc#population and sample variance/std deviationVariance measures how far each data point in the set is from the mean andthus from every other point in the set. It is the average of the squared differences from the mean.Population variance is calculated when you have data for the entire population.It gives a measure of the dispersion of […]
FAISS LangChain
FAISS (Facebook AI Similarity Search) is a vector library developed by Facebook that is used to store and search embeddings efficiently. It is particularly useful for tasks like question answering within documents, where you need to retrieve relevant parts of the content based on semantic similarity. By converting text into embeddings, FAISS allows you to […]
hyperparameter tuning with scikit learn
We would be looking at tuning hyperparameters with Scikit-Learn. Scikit-Learn is a powerful machine learning library for Python. It provides simple , efficient tools for data analysis and modeling. Hyperparameter tuning is the process of finding the best values for the settings of a machine learning model that are not learned from data, but set […]