Forecasting Sales Of Items In Retail Chains

Last Updated on May 3, 2021

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  • For the given product details (category, department, store) and historical sales, forecasting sales for the further 28 days.
  • The dataset consist of sales of previous 1941 days sales of 3049 items in 10 stores of 3 states in US.
  • Apart from historical sales data we also have rate of each item at corresponding store and dates information like events on that corresponding date.
  • I tried XGBoost, CatBoostT and LightGBM and got better score for CatBoost.
  • Deployed the model using Flask Api
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Machine Learning Algorithms

Last Updated on May 3, 2021

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I have created this projects by learning some machine learning algorithm's

Different algorithms I have learned are :

  1. K-means : In k-means algorithm I learned the working of algorithm using a dataset and loaded it. I have used sklearn and used metrics function in it to predict best fit score for dataset.
  2. KNN : In this algorithm I have used a car.data csv file in order to perform operations on it. I have trained the data and then labels of columns in order to fit the data in model. Then have predicted the acurracy of model.
  3. Regression : In this algorithm I have used different libraries such as numpy, pandas, sklearn and matplotlib for working on excel file. numpy and pandas is used for reading the csv file form directory and also to use series and dataframes of pandas. Matplotlib is used for graphical representation of model and sklearn is used to import its linear model for the data and train the data.
  4. SVM(Support Vector Machine Algorithm): In this algorithm I have used a different data set. Loaded that dataset into the algorithm with help of sklearn. works for classification and for regression, svm uses hyperplay to divide data in straights(line, 4D). Its a linear way to divide data.
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Interact With Quantum Computing Hardware Devices Using Amazon Bracket

Last Updated on May 3, 2021

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The Amazon Braket Python SDK is an open source library that provides a framework that you can use to interact with quantum computing hardware devices through Amazon Braket.

Prerequisites

Before you begin working with the Amazon Braket SDK, make sure that you've installed or configured the following prerequisites.


Python 3.7.2 or greater

Download and install Python 3.7.2 or greater from Python.org.


Git

Install Git from https://git-scm.com/downloads. Installation instructions are provided on the download page.


IAM user or role with required permissions

As a managed service, Amazon Braket performs operations on your behalf on the AWS hardware that is managed by Amazon Braket. Amazon Braket can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation.

The Braket Python SDK should not require any additional permissions aside from what is required for using Braket. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole.

To learn more about IAM user, roles, and policies, see Adding and Removing IAM Identity Permissions.


Boto3 and setting up AWS credentials

Follow the installation instructions for Boto3 and setting up AWS credentials.

Note: Make sure that your AWS region is set to one supported by Amazon Braket. You can check this in your AWS configuration file, which is located by default at ~/.aws/config.


Configure your AWS account with the resources necessary for Amazon Braket

If you are new to Amazon Braket, onboard to the service and create the resources necessary to use Amazon Braket using the AWS console.


Installing the Amazon Braket Python SDK

The Amazon Braket Python SDK can be installed with pip as follows:

pip install amazon-braket-sdk

You can also install from source by cloning this repository and running a pip install command in the root directory of the repository:

git clone https://github.com/aws/amazon-braket-sdk-python.git
cd amazon-braket-sdk-python
pip install .


Check the version you have installed

You can view the version of the amazon-braket-sdk you have installed by using the following command:

pip show amazon-braket-sdk

You can also check your version of amazon-braket-sdk from within Python:

>>> import braket._sdk as braket_sdk
>>> braket_sdk.__version__

Usage


Running a circuit on an AWS simulator

import boto3
from braket.aws import AwsDevice
from braket.circuits import Circuit

device = AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1")
s3_folder = ("amazon-braket-Your-Bucket-Name", "folder-name") # Use the S3 bucket you created during onboarding

bell = Circuit().h(0).cnot(0, 1)
task = device.run(bell, s3_folder, shots=100)
print(task.result().measurement_counts)

The code sample imports the Amazon Braket framework, then defines the device to use (the SV1 AWS simulator). The s3_folder statement defines the Amazon S3 bucket for the task result and the folder in the bucket to store the task result. This folder is created when you run the task. It then creates a Bell Pair circuit, executes the circuit on the simulator and prints the results of the job.

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Python Project: Pillow, Tesseract, And Opencv

Last Updated on May 3, 2021

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The Project

Take a ZIP file) of images and process them, using a library built into python that you need to learn how to use. A ZIP file takes several different files and compresses them, thus saving space, into one single file. The files in the ZIP file we provide are newspaper images (like you saw in week 3). Your task is to write python code which allows one to search through the images looking for the occurrences of keywords and faces. E.g. if you search for "pizza" it will return a contact sheet of all of the faces which were located on the newspaper page which mentions "pizza". This will test your ability to learn a new (library), your ability to use OpenCV to detect faces, your ability to use tesseract to do optical character recognition, and your ability to use PIL to composite images together into contact sheets.

Each page of the newspapers is saved as a single PNG image in a file called images.zip. These newspapers are in english, and contain a variety of stories, advertisements and images. Note: This file is fairly large (~200 MB) and may take some time to work with, I would encourage you to use small_img.zip for testing.

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Telecom Churn Prediction

Last Updated on May 3, 2021

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This case requires trainees to develop a model for predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company. Data for the case are available in csv format. The data are a scaled down version of the full database generously donated by an anonymous wireless telephone company. There are still 7043 customers in the database, and 20 potential predictors. Candidates can use whatever method they wish to develop their machine learning model. The data are available in one data file with 7043 rows that combines the calibration and validation customers. “calibration” database consisting of 4000 customers and a “validation” database consisting of 3043 customers. Each database contained (1) a “churn” variable signifying whether the customer had left the company two months after observation, and (2) a set of 20 potential predictor variables that could be used in a predictive churn model. Following usual model development procedures, the model would be estimated on the calibration data and tested on the validation data. This case requires both statistical analysis and creativity/judgment. I recommend you pend much time on both fine-tuning and interpreting results of your machine learning model.

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Resume Up-Loader

Last Updated on May 3, 2021

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Description:-

Ever you apply to an organisation with cv through mail but it might happen that specific organisation don't know that actually candidate need like job preference or type of job, so it get easier when we use this app called resume up-loader.

working model:-

It is my first self project using Django (python

framework) called Resume Up-loader .

where you put every detail about yourself ,job location photos,signature,CV,after submitting the information load on the server and next page you can look all your information and download the Resume also ,i am continuously working on it and upgrading that it list all the company on that preference job location for your current qualification and skill it help the candidate to know in which company is he/she is suitable for and it also company to know their candidate batter


Under a projects section

To make this single page website I have use the python web framework called Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.

I have also use HTML to define the structure of front-end and use style tag to make this beautiful

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