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Pandas for Data Science: A Step-by-Step Guide

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  Pandas is an open-source Python library widely used for data pre-processing. This library offers numerous predefined functions for managing raw data efficiently we will discuss about it in detail using the best example. First of all, we need a dataset we can create by our own using Excel or SQL or download from various sources like Kaggle,aws, or other websites https://www.kaggle.com/datasets/saadharoon27/diwali-sales-dataset Diwali Data Set When you receive data, it often comes in raw and isn’t directly usable for machine learning models. This is where data processing comes in. Pandas is a Python library specifically designed for data analysis, organization, and cleaning. It helps in structuring and preparing the data so that it can be effectively utilized in machine learning models. With Pandas, you can easily handle data manipulation tasks such as sorting, filtering, and cleaning, making it a crucial tool for preprocessing raw data into a format suitable for machine learning a...
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From Data Collection to Model Deployment: A Step-by-Step Guide in Machine Learning

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1 Collect Data => To train we need data so we collect data from different sources like Excel, SQL, Web Scraping or many other sources... There are many sources like Kaggle or Stats, these are websites From where we can download the datasets, most of the datasets are available in CSV (Comma Separated Values), which is the Excel format that is most used in ML models. 2) Data Cleaning => Whenever we get data it is available in raw format which is not directly usable in ML purpose, it means some data is missing or there is null in data or some in wrong format. Is. Is. Are there any or some duplicates and much more. ,This type of data is called messy data. If we use it directly in ML models we may get wrong or binary predictions, confusing insights or low accuracy, so it is extremely important that we avoid using it in ML. First of all the data should be cleaned, for this we have many tools like pandas, sklearn. In Python, cloud options and many others but in initial ML journey pandas...
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What is Machine Learning?

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Machine learning (ML) is a process where we provide input to a machine, and in return, the machine generates answers or responses. However, the machine doesn't respond randomly; it relies on patterns and information provided to it in advance. It's similar to teaching a child about different fruits by showing them various examples. The child learns to distinguish between fruits based on features such as color and shape. In the same manner, we expose the machine to different features and data. The machine comprehends each feature and input. Through a combination of mathematical and statistical approaches, and with the help of programming languages like Python, the machine learns to recognize patterns within the data. This understanding enables the machine to make predictions for future inputs. Overall, the entire process is known as machine learning, where the machine learns from data, understands patterns, and applies this knowledge to make predictions. In today's world, ...
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