geekycodesco, Author at Geeky Codes- Code Like A Geek But In Style

28 Jan

How to handle categorical data in machine learning

geekycodesco0 CommentsMachine Learning, Pythonbinary encoding, categorical data, count encoding, Dataframe, handling categorical, label encoding, one-hot encoding, ordinal encoding, python code, target mean encoding

Understanding Categorical Data and its Importance in Machine Learning Categorical data is a type of data that can be divided into distinct groups or categories. In machine learning, it is common to encounter categorical data in the form of labels, such as a classification problem where the output is a set of predefined categories. Handling categorical data is an important step in preprocessing your data for machine learning, as the algorithms used in machine learning often require numerical input. One of the most common ways to handle categorical data is through encoding. Encoding involves converting categorical data into a numerical…

20 Jan

How to connect OpenAI api with python code

geekycodesco0 CommentsBlog, PythonAPI, ChatGPT, GPT3, OpenAI, Programming, Python

To connect OpenAI API with Python code, you will need to use the OpenAI Python library, which can be installed using pip: pip install openai You will also need to have an API key for the OpenAI service you want to use. You can get an API key by creating an account on the OpenAI website. Once you have the OpenAI library and an API key, you can use the following code as an example on how to connect the OpenAI API with Python: import openai # Set the API key openai.api_key = "YOUR_API_KEY" # Define the prompt prompt =…

28 Oct

Steps to Create a Tensorflow Model

geekycodesco1 CommentData Science, Machine Learning

There are 3 fundamental steps to creating a model Create a Model -> Connect the layers of NN yourself by using Sequential or Functional API or import a previously built model(Transfer Learning)Compile a Model -> Define how a model's performance should be measured(metrics) and how to improve it by using an optimizer(Adam, SGD, etc.)Fit a Model -> Model tries to find a pattern in the data. Sequential and Functional API Sequential Model: A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor. A Sequential model is not…

27 Oct

How to deal with outliers

geekycodesco0 CommentsData Science, Pandas

In this Notebook, we will describe how to deal with outliers #Importing the dataset import pandas as p import numpy as n import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import load_boston import warnings warnings.filterwarnings('ignore') boston=load_boston() #it is stored as dictionary df= p.DataFrame(boston['data'],columns=boston['feature_names']) df.head() sns.distplot(df['RM']) #As we can see outliers sns.boxplot(df['RM']) Trimming outliers from the dataset def outliers(data): IQR=data.quantile(0.75)-data.quantile(0.25) lr=data.quantile(0.25)-(1.5*IQR) #lower range hr=data.quantile(0.70)+(1.5*IQR) #higher range return data.loc[~(n.where(data<lr,True,n.where(data>hr,True,False)))] outliers(df['RM']) #as we csn there is no outliers sns.boxplot(outliers(df['RM'])) #We can find outlier with using mean and standard deviation in case of IQR def outliers(data,k): lr=data.mean()-(data.std()*k) #where n is number hr=data.mean()+(data.std()*k)…

26 Oct

What is data leakage in Machine Learning

geekycodesco0 CommentsData Science, Machine Learning

When training a machine learning model, we normally prefer selecting a generalized model which is performing well both on training and validation/test data. However, there can be a situation where the model performs well during testing but fails to achieve the same level of performance with real-world (production data) usage. For example, your model is giving 95% accuracy on test data but as soon as it productized and acts on real data, it fails to achieve the same or nearby performance. Such a discrepancy between test performance and real-world performance is often referred to as Leakage. What is Train/Test bleed?…

24 Oct

Most Popular SQL Commands everyone should know

geekycodesco0 CommentsSQL

SQL Commands:SQL Commands are instructions. It is used to communicate with the database. It is also used to perform specific tasks, functions, and queries of data. SQL can perform various tasks like creating a table. add data to tables, drop the table modify the table. set permission for users.Types of SQL Commands: There are five types of SQL Commands: Data Definition Language (DDL) DDL changes the table's structure like creating a table deleting a table, altering a table, etc.All the commands of DDL are auto-committed. That means it permanently saves all the changes in the databaseHere are some commands that…

20 Oct

Five Courses that can be finished in one week to advance Pandas skills

geekycodesco0 CommentsBlogMachine learning, Pandas, Programming, Python

𝟏. 𝐖𝐫𝐢𝐭𝐢𝐧𝐠 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐂𝐨𝐝𝐞 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬: This course will build on your knowledge of Python and the panda's library and introduce you to efficient built-in pandas functions to perform tasks faster. Link:- Get the course here 𝟐. 𝐉𝐨𝐢𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬: In this course, you will learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. Get this course here 𝟑. 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬: This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas to get data from a variety of sources, from spreadsheets of…

20 Oct

Find out the Longest Path in a matrix

geekycodesco0 CommentsInterview, Interview Questions, PythonAlgorithms, Coding, Programming, Python

Given an m-by-n matrix with positive integers, determine the length of the longest path of increasing within the matrix. For example, consider the input matrix:[1 2 34 5 67 8 9] The answer should be 5 since the longest path would be 1-2-5-6-9 def isValid(mat, i, j): return 0 <= i < len(mat) and 0 <= j < len(mat) def findLongestPath(mat, i, j): if not isValid(mat, i, j): return [] path = [] if i > 0 and mat[i - 1][j] - mat[i][j] == 1: path = findLongestPath(mat, i - 1, j) if j + 1 < len(mat) and mat[i][j…

18 Oct

Explain the central limit theorem and give examples of when you can use it in a real-world problem.

geekycodesco0 CommentsInterview, Interview Questions

The center limit theorem states that if any random variable, regardless of the distribution, is sampled a large enough time, the sample mean will be approximately normally distributed. This allows for studying the properties of any statistical distribution as long as there is a large enough sample size. Important remark from Adrian Olszewski:⚠️ we can rely on the CLT with means (because it applies to any unbiased statistic) only if expressing data in this way makes sense. And it makes sense *ONLY* in the case of unimodal and symmetric data, coming from additive processes. So forget skewed, multi-modal data with mixtures of…

17 Oct

What are the motivation behind random forests and mention two reasons why they are better than individual decision trees?

geekycodesco0 CommentsInterview, Interview Questions

The motivation behind random forest or ensemble models in general in layman's terms, Let's say we have a question/problem to solve we bring 100 people and ask each of them the question/problem and record their solution. Next, we prepare a solution which is a combination/ a mixture of all the solutions provided by these 100 people. We will find that the aggregated solution will be close to the actual solution. This is known as the "Wisdom of the crowd" and this is the motivation behind Random Forests. We take weak learners (ML models) specifically, Decision Trees in the case of…