What techniques can you use to clean noisy data?

An example to understand this concept is the identification of anomalies across data patterns in any choice of time series data. Any abnormal spike or unexplainable curve on the plot of timestamp vs a measure gives precise indication of noise in the dataset.

Cleaning noisy data in machine learning model is important for the quality results. Noisy data can be handled by missing value imputation ,outlier detection, data standardization & normalization ,data encoding ,data validation, text processing, cross validation, feature sampling and ensemble methods.

When tackling noisy data, understanding its source is crucial. I've often begun by using exploratory data analysis tools like histograms or scatterplots, which provide visual insights into data anomalies. A particular instance that sticks out to me was when, after reviewing a scatterplot, I noticed a cluster of outliers. By tracing back the data collection process using metadata, I discovered a sensor malfunction was the culprit. This experience underscored the importance of not only detecting but also understanding the root cause of data noise. Starting with visualization tools and cross-referencing with data documentation can often lead to such illuminating discoveries.

During data analysis for any data, initially raw data perceived has to be cleansed and then moving further makes sense with it. So firstly noise analysis / source of noise identification is essential, where EDA plays the main role. Which visually represents the data in which relevant important data features are highlighted along with noise if any available in it. Which would either be outliers of the dataset in higher or lower ranges. Identifying which would help a data analyst to implement particular data cleaning and data enhancement techniques based on it.

Dealing with outlier always is the major data cleansing step where box plot is the most relevant tool for identifying the outlier visually through the calculation of IQR. Similarly the easiest dealing with outlier can be done by completely removing them (trimming) Also capping is adapted where data below or beyond certain range is considered outlier and not included in study. But in condition where losing data is not affordable there are certain value assumption done. Missing value treatment where certain value are assumed calculated and included in study Discretization is another outlier handling where data are grouped and missing value are assumed to behave similarly.

Dealing with outliers has always been a critical aspect of data cleaning. From my experience, while working on a dataset with irregular spikes, I found the interquartile range method particularly effective in identifying these anomalies. By setting up thresholds based on this method, I could systematically filter out extreme values that could skew results. On another project, clustering algorithms proved invaluable, grouping data in such a way that outliers became evident. Each dataset can present its own challenges, but starting with methods like standard deviation or clustering can pave the way for cleaner, more reliable data.

Handling missing values is an integral part of data preprocessing. I remember working on a project where significant chunks of data were missing. Initially, I considered simply deleting the rows, but that would have led to a loss of critical information. Instead, I opted for imputation. Using the median value of the variable to fill in the gaps proved to be a game-changer, preserving the dataset's integrity. However, in a different scenario with a different dataset, I found model-based imputation to be more effective. The key is to understand the nature and context of the missing data before deciding on the most appropriate technique to address it.

Handling missing value in the procedure also plays crucial role, where extremely robust algorithms like KNN or random forest can be adapted to define missing value. Also classification/ regression models can be used to know missing value. Or it is handled by the analyst manually by removal of rows / column with missing value. Or also impute of missing value can also be done. So basically replacing some constant value for missing value/ replacing mean/ median for missing value or relevant equivalent info from the other data in the column are used to replace missing value.

Dealing with duplicates in a dataset reminds me of a time when I was analyzing customer feedback for retail for my college project. At first glance, the dataset seemed substantial. However, upon diving deeper, I noticed many repeated records. By simply using built-in functions to drop duplicates, the dataset shrunk considerably. Later, I stumbled upon slightly varying records which turned out to be near-duplicates, probably due to manual data entry errors. Using fuzzy matching techniques, particularly the Levenshtein distance, helped in catching these sneaky duplicates. The experience taught me always to be vigilant, as even small oversights can lead to misleading conclusions in data analysis.

The Duplicate data that are repeated, faulty ,common data are removed for a smooth analysis where stopword removal, lemmatization, normalization, standarization are common process that is adapted.

The Duplicate data that are repeated, faulty ,common data are removed for a smooth analysis where stopword removal, lemmatization, normalization, standarization are common process that is adapted.

One thing I've found helpful in my experience with data analysis is ensuring that data formats are standardized. I recall working on a project where the date format inconsistencies across various datasets led to significant discrepancies in the analysis. After scratching my head for a while, I pinpointed the issue to a mixture of date formats. Utilizing date-time functions, I standardized everything to a consistent 'YYYY-MM-DD' format. It's a simple step, but it's crucial for accurate analysis. Always ensure that the data you're working with adheres to a uniform format; it can save hours of troubleshooting down the line.

All of the above points matter during Exploratory Data Analysis phase(EDA)- 1. Handling missing values 2. Checking for outliers 3. Standardize formats. 4. Handling personal data 5. Removing redundant/duplicate data

Cleaning noisy data is essential for ensuring the accuracy and reliability of analytics, modeling, and decision-making processes. Noise in data refers to any irrelevant, erroneous, or random information. A good starting point for cleaning is visual inspection. By plotting the data, anomalies often become apparent. Additionally, computing descriptive statistics like the mean, median, standard deviation, minimum, and maximum can provide insights into potential outliers or inconsistencies. Filtering techniques can help remove unwanted noise, especially in time-series data.