Welcome to data analysis with pandas tutorial. In this tutorial i have covered all the topic of pandas and tried to explain with lesser number of words.This tutorial is totally written in jupyter notebook so that anyone can clone and run it.

Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.

Welcome to the "LLM Notebooks" repository, dedicated to exploring and understanding the capabilities of Large Language Models (LLMs). This collection of Jupyter notebooks serves as a comprehensive resource for experimenting with state-of-the-art language models, including but not limited to OpenAI's GPT-3.5.

Welcome to the Deep Learning Repository! This repository serves as a comprehensive collection of Jupyter notebooks, exploring various deep learning concepts and applications. Whether you are a beginner or an experienced deep learning enthusiast, this repository offers a wide range of practical exercises, models, and predictions.

India GDP Analysis Problem Description - I NITI Aayog: Background NITI Aayog (National Institution for Transforming India) is a policy think tank of the Government of India; it provides strategic inputs to the central and the state governments to achieve various development goals. In the past, NITI Aayog has played an important role in initiatives such as Digital India, Atal Innovation Mission and various agricultural reforms and have designed various policies in education, skill development, water management, healthcare, etc. NITI Aayog was established to replace the Planning Commission of India, which used to follow a top-down model for policy making, i.e., it typically designed policies at the central level (such as the 5-year plans). On the other hand, NITI Aayog designs policies specific to the different states or segments of the economy. Finance Minister, Arun Jaitley, made the following observation on the necessity of creating NITI Aayog, "The 65-year-old Planning Commission had become a redundant organisation. It was relevant in a command economy structure, but not any longer. India is a diversified country and its states are in various phases of economic development along with their own strengths and weaknesses. In this context, a ‘one size fits all’ approach to economic planning is obsolete...". Project Brief We are working as the chief data scientist at NITI Aayog, reporting to the CEO. The CEO has initiated a project wherein the NITI Aayog will provide top-level recommendations to the Chief Ministers (CMs) of various states, which will help them prioritise areas of development for their respective states. Since different states are in different phases of development, the recommendations should be specific to the states. The overall goal of this project is to help the CMs focus on areas that will foster economic development for their respective states. Since the most common measure of economic development is the GDP, we will analyse the GDP of the various states of India and suggest ways to improve it. Understanding GDP Gross domestic product (GDP) at current prices is the GDP at the market value of goods and services produced in a country during a year. In other words, GDP measures the 'monetary value of final goods and services produced by a country/state in a given period of time'. GDP can be broadly divided into goods and services produced by three sectors: the primary sector (agriculture), the secondary sector (industry), and the tertiary sector (services). It is also known as nominal GDP. More technically, (real) GDP takes into account the price change that may have occurred due to inflation. This means that the real GDP is nominal GDP adjusted for inflation. We will use the nominal GDP for this exercise. Also, we will consider the financial year 2015-16 as the base year, as most of the data required for this exercise is available for the aforementioned period. Per Capita GDP and Income Total GDP divided by the population gives the per capita GDP, which roughly measures the average value of goods and services produced per person. The per capita income is closely related to the per capita GDP (though they are not the same). In general, the per capita income increases when the per capita GDP increases, and vice-versa. For instance, in the financial year 2015-16, the per capita income of India was ₹93,293, whereas the per capita GDP of India was $1717, which roughly amounts to ₹1,11,605. Problem Description - II Data The data is sourced from https://data.gov.in/, an Open Government Data (OGD) platform of India. The download instructions are provided in the next segment. The data for GDP analysis of the Indian states is divided into two parts: Data I-A: This dataset consists of the GSDP (Gross State Domestic Product) data for the states and union territories. Data I-B: This dataset contains the distribution of GSDP among three sectors: the primary sector (agriculture), the secondary sector (industry) and the tertiary sector (services) along with taxes and subsidies. There is separate dataset for each of the states. We are expected to read the dataset for the available states and join these (in Python) if needed. There are two parts to this project. In the first part, we will analyse and compare the GDPs of various Indian states (both total and per capita). The GDP of a state is referred to as the GSDP (Gross State Domestic Product). Then, we will divide the states into four categories based on the GDP per capita, and for each of these four categories, we will analyse the sectors that contribute the most to the GDP (such as agriculture, real estate, manufacturing, etc.). In the second part, we will analyse whether GDP per capita is related to dropout rates in schools and colleges. Part-I: GDP Analysis of the Indian States For each of the following steps of analysis, choose an appropriate type of plot for comparing the data. Also, ensure that the plots are in increasing or decreasing order for better comparison. For example, if we make a bar plot to compare the GDPs of the states, ensure that the bars are in either increasing or decreasing order of GDP. Part I-A: For the analysis below, use the Data I-A. First, we need to load the data in Python properly and then clean it. This also involves the treatment of missing values, we can choose to drop the row or column as well. Remember this will affect our next analysis and results drastically. Plot a graph for rows " % Growth over previous year" for all the states (not union territories) whose data is available, use as much data as possible for this exercise. Use the best fit line to represent the growth for each state. Draw a similar line graph for the nation as well. How will we compare the growth rates of any two states? Which states have been growing consistently fast, and which ones have been struggling? Rank top 3 fastest and 3 slowest-growing states. What is the Nation's growth rate? What has been the growth rate of my home state, and how does it compare to the national growth rate? Plot the total GDP of the states for the year 2015-16: Which Plot will we use for this? Why? (Remeber to plot the graph in a way such as it is easier to read and compare) Identify the top 5 and the bottom 5 states based on total GDP. What insights can we draw from this graph? What states are performing poorly? (Remember: this will not be solely based on total GDP) Part I-B: For the analysis below, use Data I-B. We can also use Data I-B along with Data I-A if required. Also, perform the analysis only for the duration 2014-15. Filter out the union territories (Delhi, Chandigarh, Andaman and Nicobar Islands, etc.) for further analysis, as they are governed directly by the central, not state governments. Plot the GDP per capita for all the states. Identify the top 5 and the bottom 5 states based on the GDP per capita. Find the ratio of the highest per capita GDP to the lowest per capita GDP. Plot the percentage contribution of the primary, secondary and tertiary sectors as a percentage of the total GDP for all the states. Which plot will we use here? Why? Why is (Primary + Secondary + Tertiary) not equal to total GDP? Can we draw any insight from this? Find correlation of percentile of the state (% of states with lower per capita GDP) and %contribution of Primary sector to total GDP. Categorise the states into four groups based on the GDP per capita (C1, C2, C3, C4, where C1 would have the highest per capita GDP and C4, the lowest). The quantile values are (0.20,0.5, 0.85, 1), i.e., the states lying between the 85th and the 100th percentile are in C1; those between the 50th and the 85th percentiles are in C2, and so on. Note: Categorisation into four groups will simplify the subsequent analysis, as otherwise, comparing the data of all the states would become quite exhaustive. For each category (C1, C2, C3, C4): Find the top 3/4/5 sub-sectors (such as agriculture, forestry and fishing, crops, manufacturing etc., not primary, secondary and tertiary) that contribute to approximately 80% of the GSDP of each category. Note-I: The nomenclature for this project is as follows: primary, secondary and tertiary are named 'sectors', while agriculture, manufacturing etc. are named 'sub-sectors'. Note-II: If the top 3 sub-sectors contribute to, say, 79% of the GDP of some category, we can report "These top 3 sub-sectors contribute to approximately 80% of the GDP". This is to simplify the analysis and make the results consumable. (Remember, the CEO has to present the report to the CMs, and CMs have limited time; so, the analysis needs to be sharp and concise.) Plot the contribution of the sub-sectors as a percentage of the GSDP of each category. Now that we have summarised the data in the form of plots, tables, etc., try to draw non-obvious insights from it. Think about questions such as: How does the GDP distribution of the top states (C1) differ from the others? Which sub-sectors seem to be correlated with high GDP? Which sub-sectors do the various categories need to focus on? Ask other such relevant questions, which we think are important, and note we insights for category separately. More insights are welcome and will be awarded accordingly. Finally, provide at least two recommendations for each category to improve the per capita GDP. Part-II: GDP and Education Dropout Rates In Part-I, we would have noticed that (one) way to increase per capita GDP is by shifting the distribution of GDP towards the secondary and tertiary sectors, i.e., the manufacturing and services industries. But these industries can thrive only when there is an availability of educated and skilled labour. In this part of the analysis, we will investigate whether there is any relationship between per capita GDP with dropout rates in education. Data Data II: This section will require the dropout rate dataset apart from the dataset that we used in Part-1 of the case study. Download instructions are provided in the next segment. Part-II: GDP and Education Analyse if there is any correlation of GDP per capita with dropout rates in education (primary, upper primary and secondary) for the year 2014-2015 for each state. Choose an appropriate plot to conduct this analysis. Is there any correlation between dropout rate and %contribution of each sector (Primary, Secondary and Tertiary) to the total GDP? We have the total population of each state from the data in part I. Is there any correlation between dropout rates and population? What is the expected trend and what is the observation? Write down the key insights we draw from this data: Form at least one reasonable hypothesis for the observations from the data About GDP analysis for India in the year for 2015-16 and recommendation for the individual states for increasing the GDP by focusing on various factor. Topics python statistical-analysis data-analysis gdp-analysis Resources Readme Stars 0 stars Watchers 1 watching Forks 0 forks Releases No releases published Packages No packages published Languages Jupyter Notebook 100.0% Footer © 2022 GitHub, Inc. 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Welcome to the repository! This comprehensive collection of Python scripts and Jupyter notebooks is designed to be your go-to resource for all things related to statistical analysis. Whether you're a data scientist, researcher, student, or anyone interested in understanding and working with data, this repository has you covered.