diff --git a/Clustering/1-Visualize/README.md b/Clustering/1-Visualize/README.md index e9f273d8..5fc1600d 100644 --- a/Clustering/1-Visualize/README.md +++ b/Clustering/1-Visualize/README.md @@ -4,11 +4,16 @@ > While you're studying Machine Learning with Clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare. ## [Pre-lecture quiz](link-to-quiz-app) +### Introduction Clustering is a type of unsupervised learning that presumes that a dataset is unlabelled. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. Clustering is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music. โœ… Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes ๐Ÿงฆ๐Ÿ‘•๐Ÿ‘–๐Ÿฉฒ. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos. -### Introduction + +In real life, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans. Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed. + +> โœ… Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it? +## Getting started with clustering [Scikit-Learn offers a large array](https://scikit-learn.org/stable/modules/clustering.html) of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-Learn and their appropriate use cases: @@ -19,35 +24,134 @@ Clustering is a type of unsupervised learning that presumes that a dataset is un | Mean-shift | many, uneven clusters, inductive | | Spectral clustering | few, even clusters, transductive | | Ward hierarchical clustering | many, constrained clusters, transductive | -| Agglomerative clustering | many, constrained, non Euclidan distances, transductive | +| Agglomerative clustering | many, constrained, non Euclidean distances, transductive | | DBSCAN | non-flat geometry, uneven clusters, transductive | | OPTICS | non-flat geometry, uneven clusters with variable density, transductive | | Gaussian mixtures | flat geometry, inductive | | BIRCH | large dataset with outliers, inductive | -> ๐ŸŽ“ Let's unpack some vocabulary: +> ๐ŸŽ“ How we create clusters has a lot to do with how we gather up the data points into groups. Let's unpack some vocabulary: > -> - 'transductive' vs. 'inductive' -> - 'non-flat' vs. 'flat' geometry -> - 'distances' -> - 'constrained' -> - 'density' +> ๐ŸŽ“ ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning)) +> Transductive inference is derived from observed training cases that map to specific test cases. Inductive inference is derived from training cases that map to general rules which are only then applied to test cases. +> +> ๐ŸŽ“ ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering) +> Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either 'flat' (non-[Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) or 'non-flat' (Euclidean) geometrical methods. +> +> ๐ŸŽ“ ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf) +> Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways. +> +> ๐ŸŽ“ ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering) +> Constrained Clustering introduces 'semi-supervised' learning into this unsupervised method. The relationships between points are flagged as 'cannot link' or 'must-link' so some rules are forced on the dataset. +> +> ๐ŸŽ“ 'Density' +> Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density. ### Preparation -Open the notebook.ipynb file in this folder and append the song data +Clustering is heavily dependent on visualization, so let's get started. + +Open the notebook.ipynb file in this folder and append the song data .csv file. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data: + +```python +import matplotlib.pyplot as plt +import pandas as pd + +df = pd.read_csv("../data/nigerian-songs.csv") +df.head() +``` +Check the first few lines of data: + +Get some information about the dataframe: + +```python +df.info() +``` + + +RangeIndex: 530 entries, 0 to 529 +Data columns (total 16 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 name 530 non-null object + 1 album 530 non-null object + 2 artist 530 non-null object + 3 artist_top_genre 530 non-null object + 4 release_date 530 non-null int64 + 5 length 530 non-null int64 + 6 popularity 530 non-null int64 + 7 danceability 530 non-null float64 + 8 acousticness 530 non-null float64 + 9 energy 530 non-null float64 + 10 instrumentalness 530 non-null float64 + 11 liveness 530 non-null float64 + 12 loudness 530 non-null float64 + 13 speechiness 530 non-null float64 + 14 tempo 530 non-null float64 + 15 time_signature 530 non-null int64 +dtypes: float64(8), int64(4), object(4) +memory usage: 66.4+ KB + +It's useful that this data is mostly numeric, so it's almost ready for clustering. + +Check for null values: + +```python +df.isnull().sum() +``` + +Looking good: + +name 0 +album 0 +artist 0 +artist_top_genre 0 +release_date 0 +length 0 +popularity 0 +danceability 0 +acousticness 0 +energy 0 +instrumentalness 0 +liveness 0 +loudness 0 +speechiness 0 +tempo 0 +time_signature 0 +dtype: int64 + +Describe the data: + +```python +df.describe() +``` +## Visualize the data + +Now, find out the most popular genre using a barplot: + +```python +top = df['artist_top_genre'].value_counts() +plt.figure(figsize=(10,7)) +sns.barplot(x=top[:5].index,y=top[:5].values) +plt.xticks(rotation=45) +plt.title('Top genres',color = 'blue') +``` +![most popular](images/popular.png) + +โœ… If you'd like to see more top values, change this `[:5]` to a bigger value, or remove it to see all. It's interesting that one of the top genres is called 'Missing'! + +Explore the data by checking the most popular genre: ---- -## ๐Ÿš€Challenge -Add a challenge for students to work on collaboratively in class to enhance the project +## ๐Ÿš€Challenge -Optional: add a screenshot of the completed lesson's UI if appropriate ## [Post-lecture quiz](link-to-quiz-app) ## Review & Self Study +Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped? + **Assignment**: [Assignment Name](assignment.md) diff --git a/Clustering/1-Visualize/images/popular.png b/Clustering/1-Visualize/images/popular.png new file mode 100644 index 00000000..b80f3033 Binary files /dev/null and b/Clustering/1-Visualize/images/popular.png differ diff --git a/Clustering/1-Visualize/notebook.ipynb b/Clustering/1-Visualize/notebook.ipynb index c55a5fe8..46c72bc1 100644 --- a/Clustering/1-Visualize/notebook.ipynb +++ b/Clustering/1-Visualize/notebook.ipynb @@ -22,100 +22,11 @@ "nbformat_minor": 2, "cells": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " popularity loudness danceability\n", - "0 48 -6.699 0.666\n", - "1 30 -5.640 0.710\n", - "2 40 -7.127 0.836\n", - "3 14 -4.961 0.894\n", - "4 25 -6.044 0.702" - ], - "text/html": "
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" - }, - "metadata": {}, - "execution_count": 37 - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from sklearn.cluster import KMeans\n", - "\n", - "plt.style.use(\"seaborn-whitegrid\")\n", - "plt.rc(\"figure\", autolayout=True)\n", - "plt.rc(\n", - " \"axes\",\n", - " labelweight=\"bold\",\n", - " labelsize=\"large\",\n", - " titleweight=\"bold\",\n", - " titlesize=14,\n", - " titlepad=10,\n", - ")\n", - "\n", - "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n", - "X = df.loc[:, [\"popularity\", \"loudness\", \"danceability\"]]\n", - "X.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " popularity loudness danceability Cluster\n", - "0 48 -6.699 0.666 5\n", - "1 30 -5.640 0.710 3\n", - "2 40 -7.127 0.836 3\n", - "3 14 -4.961 0.894 0\n", - "4 25 -6.044 0.702 1" - ], - "text/html": "
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" - }, - "metadata": {}, - "execution_count": 38 - } - ], "source": [ - "kmeans = KMeans(n_clusters=6)\n", - "X[\"Cluster\"] = kmeans.fit_predict(X)\n", - "X[\"Cluster\"] = X[\"Cluster\"].astype(\"category\")\n", - "\n", - "X.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": {} - } + "# Nigerian Music scraped from Spotify - an analysis" ], - "source": [ - "sns.relplot(\n", - " x=\"popularity\", y=\"danceability\", hue=\"Cluster\", data=X, height=6,\n", - ");" - ] + "cell_type": "markdown", + "metadata": {} }, { "cell_type": "code", diff --git a/Clustering/1-Visualize/solution/notebook.ipynb b/Clustering/1-Visualize/solution/notebook.ipynb index e69de29b..6399e11b 100644 --- a/Clustering/1-Visualize/solution/notebook.ipynb +++ b/Clustering/1-Visualize/solution/notebook.ipynb @@ -0,0 +1,231 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.0 64-bit ('3.7')" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Nigerian Music scraped from Spotify - an analysis" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name album \\\n", + "0 Sparky Mandy & The Jungle \n", + "1 shuga rush EVERYTHING YOU HEARD IS TRUE \n", + "2 LITT! LITT! \n", + "3 Confident / Feeling Cool Enjoy Your Life \n", + "4 wanted you rare. \n", + "\n", + " artist artist_top_genre release_date length popularity \\\n", + "0 Cruel Santino alternative r&b 2019 144000 48 \n", + "1 Odunsi (The Engine) afropop 2020 89488 30 \n", + "2 AYLร˜ indie r&b 2018 207758 40 \n", + "3 Lady Donli nigerian pop 2019 175135 14 \n", + "4 Odunsi (The Engine) afropop 2018 152049 25 \n", + "\n", + " danceability acousticness energy instrumentalness liveness loudness \\\n", + "0 0.666 0.8510 0.420 0.534000 0.1100 -6.699 \n", + "1 0.710 0.0822 0.683 0.000169 0.1010 -5.640 \n", + "2 0.836 0.2720 0.564 0.000537 0.1100 -7.127 \n", + "3 0.894 0.7980 0.611 0.000187 0.0964 -4.961 \n", + "4 0.702 0.1160 0.833 0.910000 0.3480 -6.044 \n", + "\n", + " speechiness tempo time_signature \n", + "0 0.0829 133.015 5 \n", + "1 0.3600 129.993 3 \n", + "2 0.0424 130.005 4 \n", + "3 0.1130 111.087 4 \n", + "4 0.0447 105.115 4 " + ], + "text/html": "
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namealbumartistartist_top_genrerelease_datelengthpopularitydanceabilityacousticnessenergyinstrumentalnesslivenessloudnessspeechinesstempotime_signature
0SparkyMandy & The JungleCruel Santinoalternative r&b2019144000480.6660.85100.4200.5340000.1100-6.6990.0829133.0155
1shuga rushEVERYTHING YOU HEARD IS TRUEOdunsi (The Engine)afropop202089488300.7100.08220.6830.0001690.1010-5.6400.3600129.9933
2LITT!LITT!AYLร˜indie r&b2018207758400.8360.27200.5640.0005370.1100-7.1270.0424130.0054
3Confident / Feeling CoolEnjoy Your LifeLady Donlinigerian pop2019175135140.8940.79800.6110.0001870.0964-4.9610.1130111.0874
4wanted yourare.Odunsi (The Engine)afropop2018152049250.7020.11600.8330.9100000.3480-6.0440.0447105.1154
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" + }, + "metadata": {}, + "execution_count": 33 + } + ], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\nRangeIndex: 530 entries, 0 to 529\nData columns (total 16 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 name 530 non-null object \n 1 album 530 non-null object \n 2 artist 530 non-null object \n 3 artist_top_genre 530 non-null object \n 4 release_date 530 non-null int64 \n 5 length 530 non-null int64 \n 6 popularity 530 non-null int64 \n 7 danceability 530 non-null float64\n 8 acousticness 530 non-null float64\n 9 energy 530 non-null float64\n 10 instrumentalness 530 non-null float64\n 11 liveness 530 non-null float64\n 12 loudness 530 non-null float64\n 13 speechiness 530 non-null float64\n 14 tempo 530 non-null float64\n 15 time_signature 530 non-null int64 \ndtypes: float64(8), int64(4), object(4)\nmemory usage: 66.4+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "name 0\n", + "album 0\n", + "artist 0\n", + "artist_top_genre 0\n", + "release_date 0\n", + "length 0\n", + "popularity 0\n", + "danceability 0\n", + "acousticness 0\n", + "energy 0\n", + "instrumentalness 0\n", + "liveness 0\n", + "loudness 0\n", + "speechiness 0\n", + "tempo 0\n", + "time_signature 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " release_date length popularity danceability acousticness \\\n", + "count 530.000000 530.000000 530.000000 530.000000 530.000000 \n", + "mean 2015.390566 222298.169811 17.507547 0.741619 0.265412 \n", + "std 3.131688 39696.822259 18.992212 0.117522 0.208342 \n", + "min 1998.000000 89488.000000 0.000000 0.255000 0.000665 \n", + "25% 2014.000000 199305.000000 0.000000 0.681000 0.089525 \n", + "50% 2016.000000 218509.000000 13.000000 0.761000 0.220500 \n", + "75% 2017.000000 242098.500000 31.000000 0.829500 0.403000 \n", + "max 2020.000000 511738.000000 73.000000 0.966000 0.954000 \n", + "\n", + " energy instrumentalness liveness loudness speechiness \\\n", + "count 530.000000 530.000000 530.000000 530.000000 530.000000 \n", + "mean 0.760623 0.016305 0.147308 -4.953011 0.130748 \n", + "std 0.148533 0.090321 0.123588 2.464186 0.092939 \n", + "min 0.111000 0.000000 0.028300 -19.362000 0.027800 \n", + "25% 0.669000 0.000000 0.075650 -6.298750 0.059100 \n", + "50% 0.784500 0.000004 0.103500 -4.558500 0.097950 \n", + "75% 0.875750 0.000234 0.164000 -3.331000 0.177000 \n", + "max 0.995000 0.910000 0.811000 0.582000 0.514000 \n", + "\n", + " tempo time_signature \n", + "count 530.000000 530.000000 \n", + "mean 116.487864 3.986792 \n", + "std 23.518601 0.333701 \n", + "min 61.695000 3.000000 \n", + "25% 102.961250 4.000000 \n", + "50% 112.714500 4.000000 \n", + "75% 125.039250 4.000000 \n", + "max 206.007000 5.000000 " + ], + "text/html": "
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count530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000
mean2015.390566222298.16981117.5075470.7416190.2654120.7606230.0163050.147308-4.9530110.130748116.4878643.986792
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min1998.00000089488.0000000.0000000.2550000.0006650.1110000.0000000.028300-19.3620000.02780061.6950003.000000
25%2014.000000199305.0000000.0000000.6810000.0895250.6690000.0000000.075650-6.2987500.059100102.9612504.000000
50%2016.000000218509.00000013.0000000.7610000.2205000.7845000.0000040.103500-4.5585000.097950112.7145004.000000
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+ }, + "metadata": {} + } + ], + "source": [ + "top = df['artist_top_genre'].value_counts()\n", + "plt.figure(figsize=(10,7))\n", + "sns.barplot(x=top[:5].index,y=top[:5].values)\n", + "plt.xticks(rotation=45)\n", + "plt.title('Top genres',color = 'blue')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/Clustering/2-K-Means/solution/notebook.ipynb b/Clustering/2-K-Means/solution/notebook.ipynb index e69de29b..edce473c 100644 --- a/Clustering/2-K-Means/solution/notebook.ipynb +++ b/Clustering/2-K-Means/solution/notebook.ipynb @@ -0,0 +1,135 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.0 64-bit ('3.7')" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Nigerian Music scraped from Spotify - an analysis" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "output_type": "error", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'seaborn'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'" + ] + } + ], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.cluster import KMeans\n", + "\n", + "plt.style.use(\"seaborn-whitegrid\")\n", + "plt.rc(\"figure\", autolayout=True)\n", + "plt.rc(\n", + " \"axes\",\n", + " labelweight=\"bold\",\n", + " labelsize=\"large\",\n", + " titleweight=\"bold\",\n", + " titlesize=14,\n", + " titlepad=10,\n", + ")\n", + "\n", + "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n", + "X = df.loc[:, [\"popularity\", \"loudness\", \"danceability\"]]\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " popularity loudness danceability Cluster\n", + "0 48 -6.699 0.666 5\n", + "1 30 -5.640 0.710 3\n", + "2 40 -7.127 0.836 3\n", + "3 14 -4.961 0.894 0\n", + "4 25 -6.044 0.702 1" + ], + "text/html": "
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popularityloudnessdanceabilityCluster
048-6.6990.6665
130-5.6400.7103
240-7.1270.8363
314-4.9610.8940
425-6.0440.7021
\n
" + }, + "metadata": {}, + "execution_count": 38 + } + ], + "source": [ + "kmeans = KMeans(n_clusters=6)\n", + "X[\"Cluster\"] = kmeans.fit_predict(X)\n", + "X[\"Cluster\"] = X[\"Cluster\"].astype(\"category\")\n", + "\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.relplot(\n", + " x=\"popularity\", y=\"danceability\", hue=\"Cluster\", data=X, height=6,\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/Clustering/images/turntable.jpg b/Clustering/images/turntable.jpg index df676a0e..df77b3e0 100644 Binary files a/Clustering/images/turntable.jpg and b/Clustering/images/turntable.jpg differ diff --git a/Regression/1-Tools/README.md b/Regression/1-Tools/README.md index 76611e17..7add459d 100644 --- a/Regression/1-Tools/README.md +++ b/Regression/1-Tools/README.md @@ -58,9 +58,9 @@ According to their [website](https://scikit-learn.org/stable/getting_started.htm > ๐ŸŽ“ A machine learning **model** is a mathematical model that generates predictions given data to which it has not been exposed. It builds these predictions based on its analysis of data and extrapolating patterns. -> ๐ŸŽ“ **[Supervised Learning](https://en.wikipedia.org/wiki/Supervised_learning)** works by mapping an input to an output based on example pairs. It uses **labeled** training data to build a function to make predictions. [Download a printable Zine about Supervised Learning](https://zines.jenlooper.com/zines/supervisedlearning.html). Regression, which is covered in this group of lessons, is a type of supervised learning. +> ๐ŸŽ“ **[Supervised Learning](https://wikipedia.org/wiki/Supervised_learning)** works by mapping an input to an output based on example pairs. It uses **labeled** training data to build a function to make predictions. [Download a printable Zine about Supervised Learning](https://zines.jenlooper.com/zines/supervisedlearning.html). Regression, which is covered in this group of lessons, is a type of supervised learning. -> ๐ŸŽ“ **[Unsupervised Learning](https://en.wikipedia.org/wiki/Unsupervised_learning)** works similarly but it maps pairs using **unlabeled data**. [Download a printable Zine about Unsupervised Learning](https://zines.jenlooper.com/zines/unsupervisedlearning.html) +> ๐ŸŽ“ **[Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning)** works similarly but it maps pairs using **unlabeled data**. [Download a printable Zine about Unsupervised Learning](https://zines.jenlooper.com/zines/unsupervisedlearning.html) > ๐ŸŽ“ **[Model Fitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)** in the context of machine learning refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar. **Underfitting** and **overfitting** are common problems that degrade the quality of the model as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'. @@ -72,9 +72,9 @@ TODO: Infographic to show underfitting/overfitting like this https://miro.medium > ๐ŸŽ“ **Feature Variable** A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. -> ๐ŸŽ“ **[Training and Testing](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets) datasets** Throughout this curriculum, you will divide up a dataset into at least two parts, one large group of data for 'training' and a smaller part for 'testing'. Sometimes you'll also find a 'validation' set. A training set is the group of examples you use to train a model. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. A test dataset is another independent group of data, often gathered from the original data, that you use to confirm the performance of the built model. +> ๐ŸŽ“ **[Training and Testing](https://wikipedia.org/wiki/Training,_validation,_and_test_sets) datasets** Throughout this curriculum, you will divide up a dataset into at least two parts, one large group of data for 'training' and a smaller part for 'testing'. Sometimes you'll also find a 'validation' set. A training set is the group of examples you use to train a model. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. A test dataset is another independent group of data, often gathered from the original data, that you use to confirm the performance of the built model. -> ๐ŸŽ“ **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." [source](https://en.wikipedia.org/wiki/Feature_selection) +> ๐ŸŽ“ **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." [source](https://wikipedia.org/wiki/Feature_selection) In this course, you will use Scikit-Learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. @@ -114,7 +114,7 @@ Now, load up the X and y data. 3. In a new cell, load the diabetes dataset as data and target (X and y, loaded as a tuple). X will be a data matrix, and y will be the regression target. Add some print commands to show the shape of the data matrix and its first element: -> ๐ŸŽ“ A **tuple** is an [ordered list of elements](https://en.wikipedia.org/wiki/Tuple). +> ๐ŸŽ“ A **tuple** is an [ordered list of elements](https://wikipedia.org/wiki/Tuple). โœ… Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target? diff --git a/Regression/4-Logistic/README.md b/Regression/4-Logistic/README.md index dd29e24a..13550353 100644 --- a/Regression/4-Logistic/README.md +++ b/Regression/4-Logistic/README.md @@ -105,11 +105,11 @@ sns.catplot(x="Color", y="Item Size", Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore Logistic Regression to determine a given pumpkin's likely color. -> infographic here (an image of logistic regression's sigmoid flow, like this: https://en.wikipedia.org/wiki/Logistic_regression#/media/File:Exam_pass_logistic_curve.jpeg) +> infographic here (an image of logistic regression's sigmoid flow, like this: https://wikipedia.org/wiki/Logistic_regression#/media/File:Exam_pass_logistic_curve.jpeg) > **๐Ÿงฎ Show Me The Math** > -> Remember how Linear Regression often used ordinary least squares to arrive at a value? Logistic Regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://en.wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus: +> Remember how Linear Regression often used ordinary least squares to arrive at a value? Logistic Regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus: > > ![logistic function](images/sigmoid.png) > diff --git a/TimeSeries/1-Introduction/README.md b/TimeSeries/1-Introduction/README.md index 4e92e5fe..9380dabb 100644 --- a/TimeSeries/1-Introduction/README.md +++ b/TimeSeries/1-Introduction/README.md @@ -21,7 +21,7 @@ Before starting, however, it's useful to understand what's going on behind the s When encountering the term 'time series' you need to understand its use in several different contexts. ### Time Series -In mathematics, "a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time." An example of a time series is the daily closing value of the [Dow Jones Industrial Average](https://en.wikipedia.org/wiki/Time_series). The use of time series plots and statistical modeling is frequently encountered in signal processing, weather forecasting, earthquake prediction, and other fields where events occur and data points can be plotted over time. +In mathematics, "a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time." An example of a time series is the daily closing value of the [Dow Jones Industrial Average](https://wikipedia.org/wiki/Time_series). The use of time series plots and statistical modeling is frequently encountered in signal processing, weather forecasting, earthquake prediction, and other fields where events occur and data points can be plotted over time. ### Time Series Analysis Time Series Analysis is the analysis of the above mentioned time series data. Time series data can take distinct forms, including 'interrupted time series' which detects patterns in a time series' evolution before and after an interrupting event. The type of analysis needed for the time series depends on the nature of the data. Time series data itself can take the form of series of numbers or characters. diff --git a/TimeSeries/2-ARIMA/README.md b/TimeSeries/2-ARIMA/README.md index e7c1c5a6..aebdaaed 100644 --- a/TimeSeries/2-ARIMA/README.md +++ b/TimeSeries/2-ARIMA/README.md @@ -5,22 +5,22 @@ > A brief introduction to ARIMA models. The example is done in R, but the concepts are universal. ## [Pre-lecture quiz](link-to-quiz-app) -In the previous lesson, you learned a bit about Time Series Forecasting and loaded a dataset showing the fluctuations of electrical load over a time period. In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://en.wikipedia.org/wiki/Stationary_process). +In the previous lesson, you learned a bit about Time Series Forecasting and loaded a dataset showing the fluctuations of electrical load over a time period. In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://wikipedia.org/wiki/Stationary_process). > ๐ŸŽ“ Stationarity, from a statistical context, refers to data whose distribution does not change when shifted in time. Non-stationary data, then, shows fluctuations due to trends that must be transformed to be analyzed. Seasonality, for example, can introduce fluctuations in data and can be eliminated by a process of 'seasonal-differencing'. -> ๐ŸŽ“ [Differencing](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing) data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series."[Paper by Shixiong et al](https://arxiv.org/abs/1904.07632) +> ๐ŸŽ“ [Differencing](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing) data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series."[Paper by Shixiong et al](https://arxiv.org/abs/1904.07632) Let's unpack the parts of ARIMA to better understand how it helps us model Time Series and help us make predictions against it. ## AR - for AutoRegressive -Autoregressive models, as the name implies, look 'back' in time to analyze previous values in your data and make assumptions about them. These previous values are called 'lags'. An example would be data that shows monthly sales of pencils. Each month's sales total would be considered an 'evolving variable' in the dataset. This model is built as the "evolving variable of interest is regressed on its own lagged (i.e., prior) values." [wikipedia](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) +Autoregressive models, as the name implies, look 'back' in time to analyze previous values in your data and make assumptions about them. These previous values are called 'lags'. An example would be data that shows monthly sales of pencils. Each month's sales total would be considered an 'evolving variable' in the dataset. This model is built as the "evolving variable of interest is regressed on its own lagged (i.e., prior) values." [wikipedia](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average) ## I - for Integrated -As opposed to the similar 'ARMA' models, the 'I' in ARIMA refers to its *[integrated](https://en.wikipedia.org/wiki/Order_of_integration)* aspect. The data is 'integrated' when differencing steps are applied so as to eliminate non-stationarity. +As opposed to the similar 'ARMA' models, the 'I' in ARIMA refers to its *[integrated](https://wikipedia.org/wiki/Order_of_integration)* aspect. The data is 'integrated' when differencing steps are applied so as to eliminate non-stationarity. ## MA - for Moving Average -The [moving-average](https://en.wikipedia.org/wiki/Moving-average_model) aspect of this model refers to the output variable that is determined by observing the current and past values of lags. +The [moving-average](https://wikipedia.org/wiki/Moving-average_model) aspect of this model refers to the output variable that is determined by observing the current and past values of lags. Bottom line: ARIMA is used to make a model fit the special form of time series data as closely as possible. ### Preparation @@ -290,7 +290,7 @@ Check the accuracy of your model by testing its mean absolute percentage error ( > > ![MAPE](images/mape.png) > -> [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actualt and predictedt is divided by the actualt. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_percentage_error) +> [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actualt and predictedt is divided by the actualt. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error) If this equation is expressed in code: