Results for "problem-statement"

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S-Agrawal02 / PredPol Crime Analysis

Crimes have been severely increased in past few years, the Problem Statement includes analysis of crimes with different perspectives including utmost attributes possible and predicting via the study of nature of crimes committed. The problem statement is described to initially predict the crime-type based on location and time. We worked on data about historical crimes in California. We had close to 13,000 records of crimes with data on the date and time of the crime, its location, and its type. Common types of crime include theft, criminal damage, criminal trespass, and assault. This project took on the task of predicting the type of crime that was committed given a police report in two ways one according to time that is when crime took place and another is location that is where crime took place. From a small number of overly detailed features, in time it will give the detail that at which time slot which crime is maximum and in location it will tell at which place which type of crime is maximum. They then trained various diagram based models (Graphs and Pie charts) to classify crimes by type using the generated features. Finally, they tested the performance of their models on testing data. They conclude that predicting the type of crimes committed by time and location alone is quite difficult, but that the feature engineering greatly increases predictive power. Predictions will be made to provide local authorities with an upper hand on crime and help them plan a better strategy to tackle the same.

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tanvirakibul / Heart Disease Project

Predicting heart disease using machine learning¶ This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem definition Data Evaluation Features Modelling Experimentation 1. Problem Definition In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease There is also a version of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci 3. Evaluation If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project. 4. Features Create data dictionary age - age in years sex - (1 = male; 0 = female) cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber thalach - maximum heart rate achieved exang - exercise induced angina (1 = yes; 0 = no) oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more stress more slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots) thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising target - have disease or not (1=yes, 0=no) (= the predicted attribute)

rakesh-sahani / TodoConsoleApp

Problem Statement: Create a simple java TODO application. Features: This is a core Java (console) application, that would take input from the user. When we run the code, the user would be presented with following Main menur screen: ==================================== 1. Add a new item ("add") 2. Delete an existing item ("del") 3. Update an item ("edit") 4. List all items ("ls") 5. Exit (Or press escape key to exit) Please choose your option : ===================================== If user enters “add", then "ADD TODO ITEM" screen will be displayed on the console: User will be prompted to enter inputs like this: - ===================================== 1) Add mode: Enter title : xxx //hit enter key to display the following input line. Enter description: xxxx xxx xxx //hit enter key to save the TODO item. Conditions: > TODO title is unique throughout the system. So for duplicate title, user will be prompted with error message 'duplicate entry'. > Once data is saved, Main menu screen will be shown to the user again: If user enters (option) “del”, the delete screen would be shown: ==================================== 2) Delete mode: Please enter TODO title: ABC Are you sure you want to delete (y/n): If y is pressed, item would be deleted and same option screen would be displayed. If n is pressed, then again option screen would be displayed without deleting the item. Now on selection screen if user enters “edit". Edit screen will be displayed to user like this: ==================================== 3) Edit mode Please enter TODO title: ABC Please enter updated title: ANB1 Please enter description : Updated description After pressing enter, the item would be updated. Conditions: > Please make sure, we need to maintain the uniqueness as well. In edit screen also user should not allowed to update a title thats already there in the system. > If user enters duplicate item, an error message will be displayed to the user with proper error and user will have to provide the updated title again. After successful updation, user will be presented with Main menu screen. ==================================== For list, user can view the item in sorted way. User can enter following input: A) ls name asc/desc > This will print a list of todo items, sorted by name in order as specified in command. B) ls date_created asc/desc > This will print a list of todo items, sorted by date in order specified in command. Conditions: > Any time user hits the "escape" key - the program will be terminated. > Please use proper OOPS concepts and collections as per the use case.

ShahadShaikh / Hive Case Study

Problem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.

klimanyusuf / Combating Twitter Hate Speech Using ML And NLP

Using NLP and ML, make a model to identify hate speech (racist or sexist tweets) in Twitter. Problem Statement: Twitter is the biggest platform where anybody and everybody can have their views heard. Some of these voices spread hate and negativity. Twitter is wary of its platform being used as a medium to spread hate. You are a data scientist at Twitter, and you will help Twitter in identifying the tweets with hate speech and removing them from the platform. You will use NLP techniques, perform specific cleanup for tweets data, and make a robust model. Domain: Social Media Analysis to be done: Clean up tweets and build a classification model by using NLP techniques, cleanup specific for tweets data, regularization and hyperparameter tuning using stratified k-fold and cross validation to get the best model. Content: id: identifier number of the tweet Label: 0 (non-hate) /1 (hate) Tweet: the text in the tweet Tasks: Load the tweets file using read_csv function from Pandas package. Get the tweets into a list for easy text cleanup and manipulation. To cleanup: Normalize the casing. Using regular expressions, remove user handles. These begin with '@’. Using regular expressions, remove URLs. Using TweetTokenizer from NLTK, tokenize the tweets into individual terms. Remove stop words. Remove redundant terms like ‘amp’, ‘rt’, etc. Remove ‘#’ symbols from the tweet while retaining the term. Extra cleanup by removing terms with a length of 1. Check out the top terms in the tweets: First, get all the tokenized terms into one large list. Use the counter and find the 10 most common terms. Data formatting for predictive modeling: Join the tokens back to form strings. This will be required for the vectorizers. Assign x and y. Perform train_test_split using sklearn. We’ll use TF-IDF values for the terms as a feature to get into a vector space model. Import TF-IDF vectorizer from sklearn. Instantiate with a maximum of 5000 terms in your vocabulary. Fit and apply on the train set. Apply on the test set. Model building: Ordinary Logistic Regression Instantiate Logistic Regression from sklearn with default parameters. Fit into the train data. Make predictions for the train and the test set. Model evaluation: Accuracy, recall, and f_1 score. Report the accuracy on the train set. Report the recall on the train set: decent, high, or low. Get the f1 score on the train set. Looks like you need to adjust the class imbalance, as the model seems to focus on the 0s. Adjust the appropriate class in the LogisticRegression model. Train again with the adjustment and evaluate. Train the model on the train set. Evaluate the predictions on the train set: accuracy, recall, and f_1 score. Regularization and Hyperparameter tuning: Import GridSearch and StratifiedKFold because of class imbalance. Provide the parameter grid to choose for ‘C’ and ‘penalty’ parameters. Use a balanced class weight while instantiating the logistic regression. Find the parameters with the best recall in cross validation. Choose ‘recall’ as the metric for scoring. Choose stratified 4 fold cross validation scheme. Fit into the train set. What are the best parameters? Predict and evaluate using the best estimator. Use the best estimator from the grid search to make predictions on the test set. What is the recall on the test set for the toxic comments? What is the f_1 score?

mohittomar2008 / Global Power Plant Database

Problem Statement: Description The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. The database covers approximately 35,000 power plants from 167 countries and includes thermal plants (e.g. coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g. hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. It will be continuously updated as data becomes available. Key attributes of the database The database includes the following indicators: `country` (text): 3 character country code corresponding to the ISO 3166-1 alpha-3 specification [5] `country_long` (text): longer form of the country designation `name` (text): name or title of the power plant, generally in Romanized form `gppd_idnr` (text): 10 or 12 character identifier for the power plant `capacity_mw` (number): electrical generating capacity in megawatts `latitude` (number): geolocation in decimal degrees; WGS84 (EPSG:4326) `longitude` (number): geolocation in decimal degrees; WGS84 (EPSG:4326) `primary_fuel` (text): energy source used in primary electricity generation or export `other_fuel1` (text): energy source used in electricity generation or export `other_fuel2` (text): energy source used in electricity generation or export `other_fuel3` (text): energy source used in electricity generation or export `commissioning_year` (number): year of plant operation, weighted by unit-capacity when data is available `owner` (text): majority shareholder of the power plant, generally in Romanized form `source` (text): entity reporting the data; could be an organization, report, or document, generally in Romanized form `url` (text): web document corresponding to the `source` field `geolocation_source` (text): attribution for geolocation information `wepp_id` (text): a reference to a unique plant identifier in the widely-used PLATTS-WEPP database. `year_of_capacity_data` (number): year the capacity information was reported `generation_gwh_2013` (number): electricity generation in gigawatt-hours reported for the year 2013 `generation_gwh_2014` (number): electricity generation in gigawatt-hours reported for the year 2014 `generation_gwh_2015` (number): electricity generation in gigawatt-hours reported for the year 2015 `generation_gwh_2016` (number): electricity generation in gigawatt-hours reported for the year 2016 `generation_gwh_2017` (number): electricity generation in gigawatt-hours reported for the year 2017 `generation_gwh_2018` (number): electricity generation in gigawatt-hours reported for the year 2018 `generation_gwh_2019` (number): electricity generation in gigawatt-hours reported for the year 2019 `generation_data_source` (text): attribution for the reported generation information `estimated_generation_gwh_2013` (number): estimated electricity generation in gigawatt-hours for the year 2013 `estimated_generation_gwh_2014` (number): estimated electricity generation in gigawatt-hours for the year 2014 `estimated_generation_gwh_2015` (number): estimated electricity generation in gigawatt-hours for the year 2015 `estimated_generation_gwh_2016` (number): estimated electricity generation in gigawatt-hours for the year 2016 `estimated_generation_gwh_2017` (number): estimated electricity generation in gigawatt-hours for the year 2017 'estimated_generation_note_2013` (text): label of the model/method used to estimate generation for the year 2013 `estimated_generation_note_2014` (text): label of the model/method used to estimate generation for the year 2014 `estimated_generation_note_2015` (text): label of the model/method used to estimate generation for the year 2015 `estimated_generation_note_2016` (text): label of the model/method used to estimate generation for the year 2016 `estimated_generation_note_2017` (text): label of the model/method used to estimate generation for the year 2017 Fuel Type Aggregation We define the "Fuel Type" attribute of our database based on common fuel categories. Prediction : Make two prediction 1) Primary Fuel 2) capacity_mw

BoysGmNg / EksvancRawGithubLicense

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Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: <program> Copyright (C) <year> <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <https://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <https://www.gnu.org/licenses/why-not-lgpl.html>.

saif86 / UML Class Diagram Banking System

Converts a problem statement into class diagram.

universal

c-plus-plusc-plusplusclass+10

Updated 3y ago

SaileshRamesh / RestAssuredAPIFramework

Problem Statement: Automating the Location Maps API using Rest Assured and JAVA with BDD Framework.

universal

Updated 1y ago

tranHieuDev23 / Solution Spoj Cf

Problem statements, solutions and codes example of SPOJ and Codeforces problems I have done

universal

Updated 6y ago

mathworks / Optimization For Model Aircrafts

This repository contains code that optimizes an aircraft with SAE Aero 2020 Regular Class problem statement as an example. A problem-based approach is used to construct the design optimization problem.

universal

Updated 1y ago

Arbaaz-Shaikh / IPL First Innings Score Prediction

This repository consists of all my Machine Learning Project. Datasets are provided in each of the folders above, and also the solution to the problem statements have been provided.

universal

Updated 3y ago