Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.<br>Regression Redress restrains bias by segregating the residual values.<br>My article: <a href="http://data.yt/kit/regression-redress.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">data.yt/kit/regression-redress</span><span class="invisible">.html</span></a></p><p><a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>distributions</span></a> <a href="https://hachyderm.io/tags/accuracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>accuracy</span></a> <a href="https://hachyderm.io/tags/RegressionRedress" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RegressionRedress</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a></p>
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#datadev
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Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>How to assess a statistical model?<br>How to choose between variables?</p><p>Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correlation</span></a> is irrelevant if you suspect that the relationship is not a straight line.</p><p>If monotonic relationship:<br>"<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Spearman</span></a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".<br>"<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Kendall</span></a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."<br>Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticseasily.com/kendall-t</span><span class="invisible">au-b-vs-spearman/</span></a></p><p><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>normality</span></a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>normalDistribution</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>featureEngineering</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Pearson</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regressionRedress</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>distributions</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Redressing <a href="https://hachyderm.io/tags/Bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bias</span></a>: "Correlation Constraints for Regression Models":<br>Treder et al (2021) <a href="https://doi.org/10.3389/fpsyt.2021.615754" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.3389/fpsyt.2021.615</span><span class="invisible">754</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/skLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>skLearn</span></a> <a href="https://hachyderm.io/tags/scikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitLearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span></p><p><a href="https://hachyderm.io/tags/DataViz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataViz</span></a> on two requirements:<br>* zooming, panning and rescaling<br>* shareable dashboards</p><p>"Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons", by Dr Paul Iacomi: <a href="https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pauliacomi.com/2020/06/07/plot</span><span class="invisible">ly-v-bokeh.html</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/retrieval" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>retrieval</span></a> <a href="https://hachyderm.io/tags/dataMining" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataMining</span></a> <a href="https://hachyderm.io/tags/plotly" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>plotly</span></a> <a href="https://hachyderm.io/tags/Dash" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Dash</span></a> <a href="https://hachyderm.io/tags/Bokeh" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bokeh</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/dataInteraction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataInteraction</span></a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>data</span></a> <a href="https://hachyderm.io/tags/dataDon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDon</span></a> <a href="https://hachyderm.io/tags/widgets" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>widgets</span></a> <a href="https://hachyderm.io/tags/ipython" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ipython</span></a> <a href="https://hachyderm.io/tags/jupyter" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>jupyter</span></a> <a href="https://hachyderm.io/tags/dashboards" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dashboards</span></a> <a href="https://hachyderm.io/tags/businessIntelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>businessIntelligence</span></a></p>
Eric Maugendre<p><a href="https://hachyderm.io/tags/DataViz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataViz</span></a> Decision-Making Guide</p><p>"How do you decide between <a href="https://hachyderm.io/tags/Plotly" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Plotly</span></a> and <a href="https://hachyderm.io/tags/Seaborn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Seaborn</span></a>?<br>* If you need interactive and dynamic visualizations, especially for dashboards or 3D data, Plotly is the way to go.<br>* If you’re focused on statistical analysis, creating publication-ready visuals, or conducting exploratory data analysis, Seaborn is likely your best choice."<br>by Amit Yadav: <a href="https://medium.com/@amit25173/plotly-vs-seaborn-f7207dd3e642" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">medium.com/@amit25173/plotly-v</span><span class="invisible">s-seaborn-f7207dd3e642</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/retrieval" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>retrieval</span></a> <a href="https://hachyderm.io/tags/dataMining" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataMining</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span></p><p><a href="https://hachyderm.io/tags/Lasso" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Lasso</span></a> <a href="https://hachyderm.io/tags/LinearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LinearRegression</span></a> "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"</p><p><a href="https://scikit-learn.org/stable/modules/linear_model.html#lasso" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">scikit-learn.org/stable/module</span><span class="invisible">s/linear_model.html#lasso</span></a> 🧵</p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sklearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>interpretability</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> "practitioners can leverage <a href="https://hachyderm.io/tags/LASSO" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LASSO</span></a> regression to construct more interpretable and predictive models that excel in scenarios involving high-dimensional data and intricate feature relationships."</p><p><a href="https://datasciencedecoded.com/posts/12_LASSO_Regression_Feature_Selection_Predictive_Models" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">datasciencedecoded.com/posts/1</span><span class="invisible">2_LASSO_Regression_Feature_Selection_Predictive_Models</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p>How to identify and handle duplicate values: <a href="https://stackabuse.com/handling-duplicate-values-in-a-pandas-dataframe/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">stackabuse.com/handling-duplic</span><span class="invisible">ate-values-in-a-pandas-dataframe/</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Pandas</span></a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataAnalysis</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/dataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataScience</span></a></p>
Eric Maugendre<p>A categorical variable takes on a limited number of values.<br>The categorical <a href="https://hachyderm.io/tags/dataType" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataType</span></a> is useful in the following cases:<br>- A string variable consisting of only some values. df[["label"]].astype("category") saves memory.<br>- The lexical order is not the same as the logical order (“one”, “two”, “three”). Sorting and min/max will use the logical order.<br>- As a signal to other libraries to treat as a category.</p><p>More: <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pandas.pydata.org/pandas-docs/</span><span class="invisible">stable/user_guide/categorical.html</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Pandas</span></a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataAnalysis</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a></p>
Eric Maugendre<p>"Extract Year from a datetime column", by Piyush Raj: <a href="https://datascienceparichay.com/article/pandas-extract-year-from-datetime-column/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">datascienceparichay.com/articl</span><span class="invisible">e/pandas-extract-year-from-datetime-column/</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/Pandas" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Pandas</span></a> <a href="https://hachyderm.io/tags/timeSeries" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>timeSeries</span></a> <a href="https://hachyderm.io/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>data</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/dataAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataAnalysis</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a></p>
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