In this article, I’m going to narrate how and why I started to learn even though I was an embedded software developer and had very limited spare time. “data science” In addition, I will demonstrate my progress made in such a short period of time via a “ tutorial. machine learning” I believe this article will give some inspiration to others who are willing to learn data science but don’t have enough time or . motivation Here is my story… Last year, I decided to do as a side project by building a specializing in to earn some extra money. affiliate marketing niche website juicing Nine months later I finally started to sell some products. The money I have earned so far didn’t certainly make me rich but I kept my motivation on working on the project. Although it slowed down in the past few months, the project is still going on. Besides , there was another benefit that I got from the project. making money online Thankfully, I met . data science Here is how it happened. Most of the articles I published on the site were boring “stuff”. One day, I came across a brilliant idea when I was reading an on medium.com. article In that article, the author analyzed every post about the recommended books on programming languages and ordered them by their popularity. He also published this work on an affiliate website. This small project became very successful. I followed the same principle on my own niche project. I collected over 10,000 smoothie recipes and analyzed them, ordered the most popular ingredients and published . the results I also published another on medium.com about the motivation behind the article on my niche and how I did it. article In the article, I showed how I used my to publish an easy-to-promote and informative article. python programming skills helped me collect and analyze genuine data about smoothie recipes and in the end; this helped . Python increase traffic to my website After this small success, I realized the power of the and that I had the ability to analyze it. big data Information is the oil of the 21th century, and analytics is the combustion engine. - Peter Sondergaad, Gartner Research Then I gradually fell in love with . data science After that, I decided to learn more in this field. However, I had very limited free time as I had a daytime job and have tree little monsters (kids) waiting for me at home. But I was dedicated. I worked while they were sleeping early in the morning and late at night. I got related online courses from Udemy, Coursera and dataquest.io. I read the notepads on kaggle.com. I also joined a community in Turkey in which we discuss about “deep learning” by following the courses by Andrew Ng on Coursera. The most important part: I practiced a lot on what I learned. In theory, theory and practice are the same. In practice, they’re not. - Yogi Berra I believe I made a decent progress on learning data science in this 5 months period. Now, I am able to use the following tools and libraries to some extend: : Numpy, Pandas, matplotlib, seaborn, sklearn, keras… Python libraries : Jupyter Notebook, Pycharm IDE Tools I am sure that my progress is a tiny little portion of what I should do to become a fairly good . data scientist However, I am trying hard to close the huge gap as fast as possible. Because I love the field, I’m not bored in the time I spend learning the field of data science. Now, here is the tutorial which I have promised from the beginning. The Tutorial The data I used is about my side project, juicer niche. I captured the data from amazon.com using a product advertising API and saved it in CSV format. As it is out of topic I will not cover how I get the data from Amazon. I will try to anticipate how “price”, “brand name”, “juicer type” and “color” affect the “sales rank” and to make a model using that data and python machine learning libraries. import numpy as npimport matplotlib.pyplot as pltimport matplotlib.mlab as mlabimport pandas as pdimport refrom sklearn import linear_modelfrom sklearn.model_selection import train_test_splitfrom sklearn import preprocessingfrom collections import Counter#from nltk.corpus import stopwordsimport stringimport operatorimport seaborn as snsfrom itertools import groupbyfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.neighbors import KNeighborsClassifier%matplotlib inline df_all = pd.read_csv("../out/data.csv")df_all.index = df_all['Unnamed: 0'].valuesdf_all.drop('Unnamed: 0', axis=1, inplace=True)df_all.head() Data Preperation df_all.info() <class 'pandas.core.frame.DataFrame'>Index: 1017 entries, B00004R93U to B0739LW3XSData columns (total 7 columns):brand         1010 non-null objectcategory      1017 non-null objectcolor         839 non-null objectfeatures      1017 non-null objectprice         968 non-null objectsales_rank    1017 non-null objecttitle         1017 non-null objectdtypes: object(7)memory usage: 63.6+ KB “Brand”, “category” and “color” are categorical features. “Price” and “sales_rank” are numeric features. I am going to exclude the “features” column as it is too complicated to use whilst modelling for me right now. This will be a classification problem. I will try to make a model that can predict the most successful products in terms of the sales rank. Let’s check if there are null data. df_all.isnull().sum() brand           7category        0color         178features        0price          49sales_rank      0title           0dtype: int64 Yes, there are some null data. There are also some hidden null data in “str” type as ‘None’ in sales_rank and color columns. (df_all=='None').sum() brand           0category        0color           1features        0price           0sales_rank    252title           0dtype: int64 Convert this null data to float ‘NaN’ so that isnull() function can detect them. df_all[df_all == 'None'] = float('NaN')df_all.isnull().sum() brand           7category        0color         179features        0price          49sales_rank    252title           0dtype: int64 Let’s convert the price column to float: def getPrice(x):if type(x)==str:x = x.replace(',','.')res = re.findall('\d+.\d+', x)if res:return float(res[0])else:return float('NaN')else:return x df_all['price'] = df_all['price'].apply(getPrice) Convert sales_rank column to float. Normally, I would use int. However, as the pandas doesnt have the ability to store NaN values as int, I used float. ref. https://stackoverflow.com/a/21290084 df_all['sales_rank'] = df_all['sales_rank'].astype(float) Convert the color column to lower case, and correct the typos: df_all['color'] = df_all['color'].str.lower() def correct_typo(s):if type(s) == str:typo = {"sliver": "silver", "golden": "gold", "balck":    "black", "sless": "stainless"}for k, v in typo.items():s = s.replace(k, v)return s df_all['color'] = df_all['color'].apply(correct_typo) Correct typos for ‘brand’ column, too: def correct_brands(s):if type(s) == str:typo = {"Breville Juicer": "Breville", "Omega Juicers": "Omega"}for k, v in typo.items():s = s.replace(k, v)return s df_all['brand'] = df_all['brand'].apply(correct_brands) Exploratory Data Visualization Investigate the relationship between sales rank and price. sns.lmplot(data=df_all, x='price', y='sales_rank')df_all[['sales_rank', 'price']].corr() sns.lmplot(data=df_all[df_all['price'] > 100], x='price', y='sales_rank')df_all[df_all['price'] > 100][['sales_rank', 'price']].corr() There is a positive correlation between price and the sales rank especially for the prices greater than $100. Divide sales rank into four category: srank_qcut = pd.qcut(df_all['sales_rank'], 4, labels ['0','1','2','3'])srank_qcut.head() B00004R93U    3B00004R93V    2B00004S8FH    1B00004S8FI    1B00004S8FJ    1Name: sales_rank, dtype: categoryCategories (4, object): [0 < 1 < 2 < 3] Calculate the number of the juicers in specific color categories in each sales rank category: def getColors(carr):clist = []for c in carr:if type(c) == str:lis = re.split(" |/|-|\.|,|\&", c)for l in lis:if l == '' or l == 'and' or l=='steel':continueif l == 'sliver':new_l = 'silver'elif l == 'golden':new_l = 'gold'elif l == 'balck':new_l = 'black'elif l == 'sless':new_l = 'stainless'else:new_l = lclist.append(new_l) return clist serilist = []for i in range(4):colors = df_all.loc[srank_qcut[srank_qcut == str(i)].index.tolist()]['color'].valuescolor_list = getColors(colors)color_freq = dict(Counter(color_list))serilist.append(pd.Series(color_freq))#ordered_colors = sorted(color_freq.items(), key=operator.itemgetter(1), reverse=True)df = pd.concat(serilist, axis=1) df.dropna(inplace=True) df top_color_list = df.index.tolist()plt.figure(figsize=(5,5))sns.heatmap(df, cmap='magma') Silver, black and white are most frequently found colors in better sales ranks. Explore the relationship between ‘category’ and ‘sales_rank’ columns: df_all.pivot_table(index='category', values='sales_rank') plt.figure(figsize=(15,8))sns.violinplot(x='category', y='sales_rank', data=df_all) Feature Engineering Determine brand distributions for each sales rank category: serilist = []for i in range(4):brands = df_all.loc[srank_qcut[srank_qcut == str(i)].index.tolist()]['brand'].values.tolist()color_freq = dict(Counter(brands))serilist.append(pd.Series(color_freq))df = pd.concat(serilist, axis=1) plt.figure(figsize=(5,15))sns.heatmap(df, cmap='magma') As clearly seen Omega brand is in the best position. Get the top 10 brands and convert them into dummy variables: top_brand_list = df.sort_values(by=0, ascending=False).index.tolist()[:10] arr_brand_dummy = np.zeros(df_all.shape[0] * len(top_brand_list)).reshape(df_all.shape[0], len(top_brand_list))df_brand_dummy = pd.DataFrame(arr_brand_dummy, columns=top_brand_list, index=df_all.index) for row in df_all.iterrows():b = row[1]['brand']if type(b) == str:df_brand_dummy.loc[row[0]][row[1]['brand']] = 1 df_brand_dummy.head() Get the top 10 colors and convert them into dummy variables: arr_color_dummy = np.zeros(df_all.shape[0] * len(top_color_list)).reshape(df_all.shape[0], len(top_color_list))df_color_dummy = pd.DataFrame(arr_color_dummy, columns=top_color_list, index=df_all.index) for row in df_all.iterrows():c = row[1]['color']if type(c) == str:colors = list(set(top_color_list).intersection(set(re.split(" |/|-|\.|,|\&", c))))df_color_dummy.loc[row[0]][colors] = 1 df_color_dummy.head() Create dummy variables for the category column, too: cat_dummy = pd.get_dummies(df_all['category'])cat_dummy.head() Now it is time to create target (sales_rank categories). I will assign 1 for category 0 and 0 for the remaining categories so that I get 2 classes in the target. target_sales_rank = pd.Series(np.zeros(df_all.shape[0]), index=df_all.index, name='target_sales_rank')findex = srank_qcut[srank_qcut == '0'].indextarget_sales_rank[findex] = 1 Now, create the final DataFrame: df_final = pd.concat([cat_dummy, df_brand_dummy, df_color_dummy, df_all['price'], target_sales_rank], axis=1) Drop null data: df_final.dropna(inplace=True)df_final.shape Working on the models Get input variables and output target: X = df_final.drop('target_sales_rank', axis=1)y = df_final['target_sales_rank'] Split train and test data: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) Use the following models: lm = linear_modelmodels = {'Logistic Regression': lm.LogisticRegression,'Logistic Regression CV': lm.LogisticRegressionCV,'Ridge': lm.RidgeClassifier,'Random Forest': RandomForestClassifier,'KNN': KNeighborsClassifier} Run a loop to fit and test all the models, and list the sorted scores: scores = {}for name, model in models.items():clf = model()clf.fit(X_train, y_train)score = clf.score(X_test, y_test)scores.update({name: score}) sorted_scores = sorted(scores.items(), key=operator.itemgetter(1), reverse=True)df = pd.DataFrame(sorted_scores, columns=['models', 'scores'])df Find the feature importance values for the best model Logistic Regression, and sort them in order to find out how features affect the results. clf = linear_model.LogisticRegression()clf.fit(X_train, y_train) # importance_arr = np.std(X, 0).reshape(1, X.shape[1])*clf.coef_featurelist = X.columns.tolist()importance = {}for i, imp in enumerate(importance_arr[0]):importance.update({featurelist[i]: imp}) https://stackoverflow.com/questions/34052115/how-to-find-the-importance-of-the-features-for-a-logistic-regression-model ordered_importance = sorted(importance.items(), key=operator.itemgetter(1), reverse=True) df = pd.DataFrame(ordered_importance, columns=['features','importance'])df Conclusions As clearly seen the biggest influencer is the price. It is not surprising, right! Lower price is a means of getting better sales rank in juicer market, too. As for the brand, Omega is the number one. The company has been in the market for a long period of time and has lots of successful products. They clearly have credibility and trust on their customers. In terms of color, silver and stainless steel still seem the most demanding colors. Worth to mention that the black and white options are close to them. As for the category, all of the juicer types affect the sales rank negatively. However, the masticating type has the least negative effect. This means that the masticating juicers are prone to better sales rank than centrifugal and the citrus types. This outcome is aligned with the recent popularity of the slow juicing concept. If Omega develops a new masticating juicer in silver or black color and low price, their success is guaranteed :) Final outcome:

Amazon

Target

Too Long; Didn't Read

Full Lifecycle API Management is Dead, Now What?

HackerNoon Mobile

How I Met and Fell in Love with Data Science

Too Long; Didn't Read

Companies Mentioned

@yahyacivelek

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