a-quick-prompt-guide

android_app_on_google_play

Project The Android App Market on Google Play Project Description Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention. This project lets you apply the skills from Manipulating DataFrames with pandas and Python Data Science Toolbox (Part 1). We recommend that you take those courses before starting this project. The data for this project was scraped from the Google Play website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows: apps.csv: contains all the details of the applications on Google Play. There are 13 features that describe a given app. user_reviews.csv: contains 100 reviews for each app, most helpful first. The text in each review has been pre-processed and attributed with three new features: Sentiment (Positive, Negative or Neutral), Sentiment Polarity and Sentiment Subjectivity. Project Tasks Google Play Store apps and reviews Data cleaning Exploring app categories Distribution of app ratings Size and price of an app Relation between app category and app price Filter out "junk" apps Popularity of paid apps vs free apps Sentiment analysis of user reviews

app-deployment

application

auto-correction

autocorrect_system

an autocorrect system

batch_gradient_descent_vs_mini_gradient_descent

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.In Batch Gradient Descent, all the training data is taken into consideration to take a single step. We take the average of the gradients of all the training examples and then use that mean gradient to update our parameters. So that’s just one step of gradient descent in one epoch.In Batch Gradient Descent we were considering all the examples for every step of Gradient Descent. But what if our dataset is very huge. Deep learning models crave for data. The more the data the more chances of a model to be good.SGD can be used for larger datasets. It converges faster when the dataset is large as it causes updates to the parameters more frequently.the average cost over the epochs in mini-batch gradient descent fluctuates because we are averaging a small number of examples at a time.

bit_coin

chat_gpt_app_solution

chfaisalnawaz

Faisal Nawaz Portfolio

churn_prediction

Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service. It is a critical prediction for many businesses because acquiring new clients often costs more than retaining existing ones.

cinemma_ticket_dataset

cleaning-data-in-python

cluster-analysis-in-python

cnn_image_classification

CNN is mainly used in image analysis tasks like Image recognition, Object detection & Segmentation.CNNs are used for image classification and recognition because of its high accuracy..The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.

cost_of_living_index_2022

Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods prices, including groceries, restaurants, transportation and utilities. Cost of Living Index does not include accommodation expenses such as rent or mortgage.

credit-card-approval

cross_validation-and-10-cross_validation

data-analysis

data-cleaning

data-manipulation

data-scienctist_track

It contains all python work

data-visualization

data.table

data_collection_data_wrangling

Data Collection & Data Wrangling course offered by Al-Nafi

database.mtcars

databasemtcar.r

datacamp_project

DataCamp projects & cheatsheet

dates-and-times-python-

digit_recognition_neural_network