Outliersงาน

...5. Top Selling Countries (Using Spark) 6. Item Costs (Using Spark) 7. Sales Yearwise (Using PySpark) 8. Orders per Item (Using PySpark) 9. Country with Highest Sales (Using PySpark) 10. Customer Segmentation: Use clustering algorithms to identify different customer segments. 11. Time Series Forecasting: Predict future sales using ARIMA or LSTM. 12. Anomaly Detection: Identify any anomalies or outliers that could indicate fraudulent activity. 13. Association Rule Mining: Find associations between different products in the data (Using Spark). 14. Price Elasticity: Understand how the demand for a product changes with a change in its price (Using PySpark). 15. Correlation Between Priority and Profit: Analyze if 'Order Priority' has any correlation with 'Total Profit�...

...5. Top Selling Countries (Using Spark) 6. Item Costs (Using Spark) 7. Sales Yearwise (Using PySpark) 8. Orders per Item (Using PySpark) 9. Country with Highest Sales (Using PySpark) 10. Customer Segmentation: Use clustering algorithms to identify different customer segments. 11. Time Series Forecasting: Predict future sales using ARIMA or LSTM. 12. Anomaly Detection: Identify any anomalies or outliers that could indicate fraudulent activity. 13. Association Rule Mining: Find associations between different products in the data (Using Spark). 14. Price Elasticity: Understand how the demand for a product changes with a change in its price (Using PySpark). 15. Correlation Between Priority and Profit: Analyze if 'Order Priority' has any correlation with 'Total Profit�...

I am looking to have some Exploratory Data Analysis, Impact of Outliers and Transformations, and Data Cleaning and Feature Engineering done. I'm open to suggestions regarding potential outliers and transformations, as I'm not sure what type of dataset will be used for the analysis. The deliverables for this project will need to include conducting an exploratory data analysis, identifying outliers and performing transformations as needed, and doing feature engineering and data cleaning to enable effective data exploration. The deliverables should also include accurate visual representations of the data to ensure accurate findings. Please take a look at the pdf file as this explains the assignment. This is a great opportunity for someone with knowledge of da...

I am looking for someone to help me carry out rule mining on a medium-sized dataset of 1000-10000 records. This project is for personal use only. My main aim in Rule Mining is to identify patterns and generate rules which I can use for predictive analysis and understanding of outliers in the data. I need someone who has experience and expertise in Rule Mining, and who can process my dataset quickly and efficiently. Also one part of this is to use auto-encoders. I expect person knowledge of both auto-encoders and rule mining.

...access control to differentiate between administrative and regular user capabilities. Dashboard Users can view a summary of their past analyses. Access to saved data sets and configurations. Profile management options, including password change and account deletion. Data Upload and Management Support for uploading data in CSV, Excel, and SPSS formats. Data validation to check for missing values, outliers, and format inconsistencies. Metadata management to store additional information about the data sets. Statistical Analysis Integration of open-source Python packages for SEM and MCDM. MCDM Analysis SEM Analysis Result Presentation Results will be presented in graphical formats such as scatter plots and path diagrams. Option to download reports in PDF and Word formats. Subscri...

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seeking an experienced data analyst to do a quantitative and qualitative analysis of the collected data. Responsibilities: Data Preparation: Clean and organize datasets for analysis. Handle missing data and identify potential outliers. Quantitative Analysis: Conduct correlation and regression analysis to examine the relationship between population growth and various environmental sustainability indicators. Utilize statistical software (preferably Stata) for data analysis. Qualitative Analysis: coding and categorizing qualitative data from interviews, surveys, and policy documents. Conduct thematic analysis to identify key themes and patterns. Reporting: Create clear and concise reports summarizing the analysis results, including visualizations and interpretations. Key Requ...

...who subscribe to term deposits ("yes") and those who do not ("no"). 2. Dataset: I will provide you with the "Bank Marketing" dataset, which includes various client attributes (e.g., age, job, marital status) and campaign-related information (e.g., contact type, last contact duration). 3. Scope of Work: Data Preprocessing: Clean, preprocess, and explore the dataset to handle missing values and outliers if any. Feature Engineering: Create relevant features or encode categorical variables as needed. Model Selection: Experiment with different machine learning algorithms, including but not limited to logistic regression, decision trees, random forests, and gradient boosting. Model Training: Train and optimize the selected model(s) using appropriate tech...

...Description: I am seeking a statistician or data analyst with proficiency in SPSS to conduct a comprehensive analysis on a questionnaire related to football coaching in Malaysia's Professional League. Details of the task include: 1. Data Entry: - Input the raw data from the questionnaire into SPSS, ensuring accuracy and integrity of the data. 2. Data Cleaning: - Check for missing values, outliers, and any other discrepancies that might affect the analysis. - Offer recommendations on how to handle any issues found. 3. Descriptive Statistics: - Provide frequency distributions, means, standard deviations, and other relevant statistics for each question. 4. Inferential Statistics: - Based on the nature of the data and the hypotheses (to be shared later), con...

As a data scientist, I am looking for an expert in normalizing flow algorithms in order to develop a project for outlier detection and make it better by improving the attention mechanism. I will provide the data for this project, which is image data and it is already labeled for training purposes. Therefore, the freelancer should ha...expert in normalizing flow algorithms in order to develop a project for outlier detection and make it better by improving the attention mechanism. I will provide the data for this project, which is image data and it is already labeled for training purposes. Therefore, the freelancer should have an expert level understanding of normalizing flow algorithms. The goal of this project is to identify outliers using normalizing flow to better understand the i...

BOOK: OUTLIERS: THE STORY OF SUCCESS BY MALCOLM GLADWELL 1: Please read the chapters 1 and 2 of the book. Summarize the key learning of each chapter in a few sentences. 2: Think of 4 examples (mentioned in the book or otherwise) arising from the above learning(s) that may relate to any part of the class. Each example may relate to a different point or to the same point. For example, you may find that the key learning relates to something we discussed about competition and/or something we discussed in branding. 3: Explain how and why these examples connect to the topic(s) you think they relate to. Write in a way that someone who has not read the book can understand the key point and how the examples relate to our class. For example, in his book David and Goliath, Gladwell...

BOOK: OUTLIERS: THE STORY OF SUCCESS BY MALCOLM GLADWELL 1: Please read the chapters 1 and 2 of the book. Summarize the key learning of each chapter in a few sentences. 2: Think of 4 examples (mentioned in the book or otherwise) arising from the above learning(s) that may relate to any part of the class. Each example may relate to a different point or to the same point. For example, you may find that the key learning relates to something we discussed about competition and/or something we discussed in branding. 3: Explain how and why these examples connect to the topic(s) you think they relate to. Write in a way that someone who has not read the book can understand the key point and how the examples relate to our class. For example, in his book David and Goliath, Gladwell...

...the Data section Your notebook should contain the following: 1) Exploratory Data Analysis Your free to explore the data and give your own EDA Some things you can include are Correlation Matrix or Heatmap Plots of your own choice that may help extract useful information 2) Statistical Data Analaysis Find the distribution of the data. You can include plots like histograms. Note the effect of outliers if any. 3) Data Cleaning and preprocessing To clean the data and make it suitable for modelling Use your preferred method of preprocessing. 4) Data Modelling To create a predictive classification model and make predictions for the data in To display the Confusion Matrix of your model. I have a specific dataset that will be used for the project, so the data is already sourced a...

...the download link if you message me. I need the project complete in 24 hours. I have some web site links that show code for some data cleansing as listed below. Remove irrelevant data Standardize capitalization Convert data type Clear formatting Fix errors Handle missing values Remove irrelevant data Remove duplicate data Fix structural errors Do type conversion Handle missing data Deal with outliers Standardize/Normalize data Validate data Column operations (Select columns, Sort columns, Drop columns, Rename columns) Handle duplicates (Distinct, Drop duplicate) Remove spaces (Trim, Left trim, Right trim) Cleaning Functions for Number fields (Is numeric, Convert, Round, Trunc) Cleaning Functions for Text fields (Capitalize, Lower, Upper, Sub strings, Reverse, Split string) Cle...

...the Microsoft downloads. I will send you the download link if you message me. I have some web site links that show code for some data cleansing as listed below. Remove irrelevant data Standardize capitalization Convert data type Clear formatting Fix errors Handle missing values Remove irrelevant data Remove duplicate data Fix structural errors Do type conversion Handle missing data Deal with outliers Standardize/Normalize data Validate data Column operations (Select columns, Sort columns, Drop columns, Rename columns) Handle duplicates (Distinct, Drop duplicate) Remove spaces (Trim, Left trim, Right trim) Cleaning Functions for Number fields (Is numeric, Convert, Round, Trunc) Cleaning Functions for Text fields (Capitalize, Lower, Upper, Sub strings, Reverse, Split string) Cl...

...preference for the software or programming language used for the analysis, so you can choose whichever you are most comfortable with, whether it be Python, R, or any other tool. The desired outcome of the analysis is to gain other specific insights from the dataset, beyond simply predicting future trends or identifying patterns. This could include uncovering hidden relationships, identifying outliers or anomalies, or any other valuable insights that can be extracted from the data. Ideal Skills and Experience: - Strong background in time series analysis - Proficiency in Python, R, or other relevant programming languages and tools - Experience working with large datasets - Knowledge of statistical modeling and forecasting techniques - Ability to communicate complex findings in a...

...agency will be responsible for sourcing property data from diverse channels, such as real estate databases, public records, APIs, and potentially employing tools like Python's BeautifulSoup or Scrapy for data scraping. 2. **Data Preprocessing:** Thorough data cleaning and preprocessing will be conducted by your team using Python libraries like Pandas, ensuring the resolution of missing values, outliers, and data inconsistencies. 3. **Feature Engineering:** Your experts will identify and engineer crucial features from the dataset, utilizing Python libraries like Pandas and NumPy to enhance the accuracy of the valuation model. 4. **Model Selection:** Your agency will make informed decisions on algorithm selection, considering options such as linear regression, decision tree...

Hello! I'm looking for a professional who can help me clean and analyze tree inventory data from a number of species, remove outliers and generate additional statistical analyses and reports. The data needs to be cleaned thoroughly and efficiently to ensure accurate analysis. Then, I would like to see detailed analysis with tables/graphs/charts generated in the reports. I'm expecting a very high-quality result for this project, as the data is valuable. Thank you for considering the project!

Require a project to work on analysing biomechanics and kinematics data. I will provide data in h5 files which you must convert, normalise, remove outliers. Then need you to calculate things like GPS and GVS. You must have experience in biomechanic analysis, Matlab etc

Job Title: Panel Data Analyst Job Description: I am seeking...for cleaning and analyzing panel data to derive meaningful insights and support data-driven decision-making processes. The ideal candidate will have a strong background in panel data analysis, statistical modeling, and data manipulation techniques. Responsibilities: - Clean and preprocess panel data to ensure data quality - Conduct exploratory data analysis to identify patterns, trends, and outliers. - Apply statistical techniques and econometric models to analyze panel data Requirements: - Strong knowledge of panel data analysis techniques and experience - Proficiency in R or Stata software for data manipulation and analysis. - Familiarity with statistical modeling techniques, such as fixed effects, random effects, ...

I am looking for an experienced data scientist to help me explore relationships between variables in a data set. I already have a rough idea of what...great opportunity for the right person to bring insight to analyzing the data and making the data driven decisions. This project requires someone who is proficient in Python as well as other scripting languages. The right person will have experience with working with large data sets and can transform this data into meaningful information. They will have an eye for spotting trends, patterns, outliers, etc. I am looking for a data analyst who can bring visibility to my project and help me get the outcome that I am expecting. If you believe you have the skills and qualifications to see this project through, then I would love to hear...

System: Lightsail, Gunicorn Flask API, Ubuntu, running as a system service, Nginx included There are three attributes to test: elapsed function time and 2 separate response times, there are outliers that occur when the Flask API hasn't been called in a while. This is the main issue to fix

I am trying to find a house for myself and I decide to collect many houses detail on an Iranian website with their updated price and this is the final data which is a kind of house pricing prediction. I need data visualization on this data set. Content There is some information about each house: Area in square meters Number of bedrooms Has Parking or not Has elevator or not Has war...is the final data which is a kind of house pricing prediction. I need data visualization on this data set. Content There is some information about each house: Area in square meters Number of bedrooms Has Parking or not Has elevator or not Has warehouse or not The region where the house is placed Price in Toman and USD Notes: Every USD is equal to 30,000 Tomans (Extra Info). Take care about outliers...

Download the dataset for the mini-challenge you've selected from: to an external site. Analyze the dataset: clean the data, look for anomalies, summary statistics, outliers, averages, etc. Anything to give you an understanding of the variables, interesting data, interesting questions. How does your data analysis help understand and answer the mini-challenge questions you selected? Update your ObservableHQ notebook and github with any and all analysis and solutions related to this work and share the file or link.

I need help fixing a couple of correlations tables in R as part of a project I'm working on for school. Some of the correlations are wonky (for example, "led a class project" had a -0.33 correlation with "current salary" for MA graduates, which seems odd), so I was told to check for outliers and adjust any that are heavily skewing the data by moving them in two standard deviations. I figured out how to make scatterplots to see the outliers, but I'm not sure how to adjust those outliers. I also need to check each correlation for statistical significance so I can mark them in my table. I've spent some time in R, but I'm not much of a stats person or a coding person, so this has been a great source of frustration. I'm atta...

...Data. List and explain the important variables. Explain everything that you do to preprocess the data. 6. Descriptive Statistics and Visualisation [Plain text & R code & Output]: Summarise the important variables in your investigation. Use visualisation to highlight interesting features of the data and tell the overall story. Explain how you dealt with data issues (if any), e.g. missing data and outliers. 7. Hypothesis Testing [Plain text & R code & Output]: Apply an appropriate hypothesis test for your investigation. Ensure you state the hypotheses and check any assumptions. Report the appropriate values and interpret the results. 8. Discussion [Plain text]: Discuss the major findings of your investigation. Discuss any strengths and limitations. Propose direction...

...The tracking module implemented is flutter_background_location by We have two requirements at present when bidding please mention you are bidding on 1, 2 or both please: 1) Python Data Cleaning Script: We get very specific data but from the current data we receive some that is not reliable to build routes from as it may indicate one is cycling when one is driving and some outliers in rural areas which should not be used. At present we use all the data disregarding the accuracy and the activity probability which we believe if cleaned or filtered correctly will display correct routes. This cleaned data should be written from one mysql table to another reduced cleansed table, from here it will be called from a webservice to do the display. We would prefer this cleaning to

...the forecast associated to it. I m expecting someone : the Input: The first step is to define the variables that need to be included in the model. and Analyze Data: Collect from my database historical data on natural gas prices, as well as other relevant variables that might impact prices, such as weather, supply, demand. Analyze the data to identify trends, patterns, and outliers that need to be taken into account. Selection: Choose the appropriate GARCH model to use for the forecasting task. This can involve testing different models and comparing their performance using statistical metrics such as AIC or BIC. Estimation: Estimate the parameters of the GARCH model based on the historical data. This involves using statistical methods such as Maximum

...the forecast associated to it. I m expecting someone : the Input: The first step is to define the variables that need to be included in the model. and Analyze Data: Collect from my database historical data on natural gas prices, as well as other relevant variables that might impact prices, such as weather, supply, demand. Analyze the data to identify trends, patterns, and outliers that need to be taken into account. Selection: Choose the appropriate GARCH model to use for the forecasting task. This can involve testing different models and comparing their performance using statistical metrics such as AIC or BIC. Estimation: Estimate the parameters of the GARCH model based on the historical data. This involves using statistical methods such as Maximum

Highlight the outliers from the given dataset (will provide upon award) using the Interquartile Range (IQR) method. The IQR is the range between the first quartile (25th percentile) and the third quartile (75th percentile). Outliers are typically values that fall below the first quartile minus 1.5 times the IQR or above the third quartile plus 1.5 times the IQR. Please refer to your own expertise. The dataset contains six (6) categories with 613 total data points. Highlight outliers using the Interquartile Range (IQR) method for each category. All calculations must be shown.

Anomaly Detection System using ARIMA (outlier) in Python I am looking for a freelancer who can help me develop an anomaly detection system that will analyze time-series data. The primary purpose of this system is to find outliers. I prefer to use Python for the implementation. I will supply a JSON document with Count being the target variable (y axis) and x-axis being the time. There will be one other variable which is simply an ID which identifies a different person. Ideal Skills and Experience: - Proficient in Python programming language - Experience in anomaly detection using ARIMA - Strong understanding of time-series data analysis - Familiarity with statistical modeling and machine learning techniques - Ability to interpret and visualize data effectively Deliverables: - A...

...Talented R Programmers, Our company is seeking a proficient R programmer to perform an in-depth Exploratory Data Analysis (EDA) on a public dataset that we will provide. We are looking for someone with a strong understanding of EDA techniques and the ability to effectively communicate the insights extracted from the dataset. Project Requirements: 1- Identify and visualize key patterns, trends, and outliers in the dataset using appropriate graphical representations (e.g., bar plots, histograms, scatterplots, etc.). 2- Perform statistical analysis to derive meaningful insights and correlations from the dataset. 3- Provide detailed documentation explaining the steps, code, and insights gained from the analysis. 4- Deliver the final analysis report and the R code in a well-structu...

Descripción: Reali...datos compilados de fuentes oficiales. El intervalo de tiempo seleccionado como muestra se deja al criterio del estudiante. • Se pide: 1. Caracterizar la información compilada, tanto las variables dependientes como la variable independiente. 2. Escoger las técnicas de preprocesamiento de la información que considere oportunas, así como también de reducción de la dimensionalidad y detección de outliers en las variables explicativas. 3. Justificar el proceso de selección de las variables independientes que han sido consideradas (e.g. Relaciones de correlación, causalidad, algoritmos de clustering). Cada paso de este inciso debe ser explicado minuciosamente. 4. El ejercicio de nowcasting se ...

design of a portable visual odometry system that utilizes a feature-based stereo visual odometry, with IMU assistance. The focus is on developing an algorithm that can provide a usable level of accur...correspondences are found using an optimized block-matching algorithm using the normalized cross-correlation metric. These disparity values are used to triangulate two-point clouds at successive steps in frames, after which a weighted least-squares pose estimation algorithm is run to estimate the pose change between timesteps. A multivariate gaussian anomaly detection algorithm is used to reject outliers due to poor matching or moving objects. Finally, a Kalman filter is exploited to enforce a constant-acceleration dynamic model on the cameras, and fuse IMU data for better attitude e...

...publicly available dataset, conduct an analysis, and present your findings with a partner (you will work in pairs). In This Project You Will Formulate a hypothesis about the data Refine the raw data into processed data using excel Conduct statistical analysis Develop visuals You will present findings, including: What you did to refine your data: outline your process! Outliers. How did you determine outliers in your dataset? What thresholds did you set? Did you have to make any additional assumptions? An overview of the statistical analysis performed, and why it was chosen What insight they gained from the statistical analysis Key visuals to represent their insight Requirements To successfully complete the project and receive a certificate you must submit A...

A pessoa vai me ajudar a análisar o projeto e então fazer uma apresentação da solução, juntamente com os tipos de analise. Ex: Analise Univariada com medidas de Posição Anailse Univariada com medidas de disperção Detecção de Outliers Etc… A analise é sobre um banco de dados de uma imobiliaria, e então deve ser feita as analises para que seja descrita/explicada o funcionamento de cada variavel das colunas. A analise tem que ser feita utilizando Numpy e Panda

...datetime. • For each categorical variable, remove the spaces, and then replace the empty string '' by NaN. • Replace the zeros in Prices, Land squares, etc. by NaN • Show a summary of all missing values as well as the summary statistics • Drop the columns 'BOROUGH', 'EASE-MENT', 'APARTMENT NUMBER' • Drop duplicates if any • Drop rows with NaN values • Identify and remove outliers if any • Show the shape of the resulting dataframe. • Consider the log of the prices and normalise the data. 2. Data Exploration. Consider the resulting dataframe. This first aggressive cleaning should give a smaller dataset, which you can start by exploring relationships between t...

I need an expert in statistical analysis to do the following steps using STATA and write the process and the results I have 6 models that require statistical analysis. The expert should have the ability to deal with outliers and do regression using up to 7 different regression methods, also, should be able to deal with the endogeneity issue and describe in a report what has been done and how. Also should write the results and discuss the findings from a statistical perspective and provide all tables and figures can to illustrate the results. The data type is panel data and all prepared and ready for analysis.

Need a skilled Power BI developer with python and R code proficiency and website integration. This is a summary. The dataset is a list of about 400,000 recent property sales. Full details will be provided in a PDF delivery: 1. I need a code that calculates a column with a binary output "Good Deal" or "Bad Deal" for sales in the last 6 months. 2. Remove outliers based on statistical precedence. 3. Compute calculated columns based on criteria such as average SP for houses with a private driveway. (Just need the appropriate code). 4. Calculate Average Rate of Change. It needs to be a dynamic measure. For example: I need properties that exist in areas with an average rate of change of 15% or more. I need the AROC for several columns. f. Create a qtr by qrtr bar c...

Pressure of both types and diabetes are adopted as dependent variables, and the rest of the variables are independent. In the theoretical side, you have the formulas for the distributions that I used, and the last thing I have to use is the Bayes factor in order to find these outliers. My target is to apply MATLAB programming using Bayes factor on a large set of data to find outliers or unreal values. ### No agent messaging me, I won't answer except direct specialists in MATLAB and can achieve this project

...dictionary to get a domain-based perspective of the database and problem statement. You also learnt how Shreyas looks at the possible values that a column can have to understand its data type and the range of values. Some things to keep in mind are: For this capstone, consider only the cases having order status as 'delivered'. Perform EDA before inferring anything from the dataset and treat the outliers and missing values developing an understanding of the problem statement and the dataset, it is important to think about your approach to solve the problem. It is always advisable to break down a complex problem into smaller, simpler ones. This is called structured problem solving As mentioned by Shreyas, the structured problem approach that you will be using here is th...

...obtain. You wish to predict Premium based on other variables. The dataset has been split into a training set and a test set. Tasks The following tasks should be performed on the training set. You are not allowed to use the test set. Data pre-processing and descriptive study. a. Provide a descriptive study of the dataset. Tables and figures could be helpful. b. Handle possible missing values, outliers, etc. Formulate the problem in the data analytic framework. a. What is the response? What are the predictors? Are they quantitative or qualitative? b. How many observations and predictors are there? c. Is this an inference or prediction problem? d. Is this a regression or classification problem? Apply data analytic techniques to solve the problem. a. Apply various data analytic mode...

design of a portable visual odometry system that utilizes a feature-based stereo visual odometry, with IMU assistance. The focus is on developing an algorithm that can provide a usable level of accur...correspondences are found using an optimized block-matching algorithm using the normalized cross-correlation metric. These disparity values are used to triangulate two-point clouds at successive steps in frames, after which a weighted least-squares pose estimation algorithm is run to estimate the pose change between timesteps. A multivariate gaussian anomaly detection algorithm is used to reject outliers due to poor matching or moving objects. Finally, a Kalman filter is exploited to enforce a constant-acceleration dynamic model on the cameras, and fuse IMU data for better attitude e...

...characteristics of new urbanism that are different from the smart growth characteristics? Do you favor smart growth/new urbanism for 21st Century urban communities? Why or why not? List them: 1, 2, 3, and then 1, 2, followed by your discussion. 2. Compare and contrast the positions/viewpoints on addressing global climate change from the various groupings of nation states, as well as any outliers. Use bullet points to compare and contrast. 3. What are 3 types/kinds of non-point source pollution adversely impacting the Chesapeake Bay today? What would you prescribe to mitigate one of them? (From your list of 3, choose 1 and discuss how you would go about “fixing” the problem.) List them: 1, 2, 3, and then discuss the one you pick. 4. Why is ...

To illustrate the practical application of automatic classification with DBSCAN. First, we will observe the behavior of the algorithm in a synthetic case involving k-means failure. We will see how to use classical heuristics to determine the parameters of the algorithm and how to interpret the partitioning obtained. Finally, we will apply DBSCAN on a real data set and we will illustrate...practical application of automatic classification with DBSCAN. First, we will observe the behavior of the algorithm in a synthetic case involving k-means failure. We will see how to use classical heuristics to determine the parameters of the algorithm and how to interpret the partitioning obtained. Finally, we will apply DBSCAN on a real data set and we will illustrate its use for the detection of out...

...Dec, but generally increases during march and May. Other data points available. Cycle time, the time the team starts to complete. Estimate number: how complex the widget is Number of people on each team My question is this, which statistical methods could used to explore the data, any possible correlations etc given its not normally distributed, though I’ve muddled with functions, removing outliers etc to normalize it. This ultimate goal would be a better forecasting model . Note: this is a personal venture and not homework :)...

...Dec, but generally increases during march and May. Other data points available. Cycle time, the time the team starts to complete. Estimate number: how complex the widget is Number of people on each team My question is this, which statistical methods could used to explore the data, any possible correlations etc given its not normally distributed, though I’ve muddled with functions, removing outliers etc to normalize it. This ultimate goal would be a better forecasting model . Note: this is a personal venture and not homework :)...

The following sections need to be analyzed: Under the All Data Cleaned tab – - I need a quick analysis through SPSS (or anything else that could facilitate the analysis) on sections E1, E2, F1, F2, F3, F4, and F5 - Each section is separate - However, if you do see some discrepancies, significant outliers, please highlight those. - I need visualization of those findings per section. However, I’d appreciate it if you could see if there are differences in findings when looking at three different provinces under section A1 (column G) or the type of location under section A5 (column K). For section E1, I’d appreciate it if you could present the analysis of only the self-reported income ranges. Please dismiss big money, little money, and some money. That’...

...plan to achieve with your project? o What is the primary goal of the project, and how will it help the business? o What are the implications of the project? 2. Describing Dataset o Where has the data been gathered? o What are the input and output variables? 3. Descriptive Analysis o Number of columns and rows o Percent of missing values by columns o Means, medians, modes, correlations, variance, outliers o Data visualizations such as scatter plots or correlation plots 4. Data Preprocessing o Variable treatment § Dropping columns and rows when necessary § Imputing missing values § Transforming variables if necessary § Variable selection o Fitting models § Fitting multiple models with varying parameters § Summarize performance indicators of models &sec...

Adapt Dot it out to allow for alternative display using stat block; Add outlier detection in dot it out.