firstName

lastName

email

updatesConsent

Stay up-to-date with my words ✍️

## request1:

Provide the list of markets in which customer "Atliq Exclusive" operates its business in the APAC region.

```
    
    select distinct(market) from dim_customer
    where customer = 'Atliq Exclusive'
    and region = 'APAC
```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>market</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>India</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Indonesia</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Japan</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Philiphines</td>
    </tr>
    <tr>
      <th>4</th>
      <td>South Korea</td>
    </tr>
    <tr>
      <th>5</th>
      <td>Australia</td>
    </tr>
    <tr>
      <th>6</th>
      <td>Newzealand</td>
    </tr>
    <tr>
      <th>7</th>
      <td>Bangladesh</td>
    </tr>
  </tbody>
</table>
</div>

## request2:

What is the percentage of unique product increase in 2021 vs. 2020? The final output contains these fields,unique\_products\_2020 unique\_products\_2021 percentage\_chg

                        with 
                        Unique_Product_2020 as(
                        select count(distinct(product_code)) as unique_product_2020
                        from fact_sales_monthly
                        where fiscal_year = 2020),
                        Unique_Product_2021 as(
                        select count(distinct(product_code)) as unique_product_2021
                        from fact_sales_monthly
                        where fiscal_year = 2021),
                        Percentage_Change as(
                        select
                        round(((unique_product_2021-unique_product_2020)/unique_product_2020)*100)
                        as pct_change 
                        from Unique_Product_2021,
                             Unique_Product_2020)
                        select * from Unique_Product_2020,
                                      Unique_Product_2021,
                                      Percentage_Change;

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>unique\_product\_2020</th>
      <th>unique\_product\_2021</th>
      <th>pct\_change</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>245</td>
      <td>334</td>
      <td>36.0</td>
    </tr>
  </tbody>
</table>
</div>

## request 3:

Provide a report with all the unique product counts for each segment and sort them in descending order ofproduct counts. The final output contains 2 fields,segment product\_count

    select segment ,count(distinct(product_code)) as product_count
    from dim_product
    group by segment 
    order by product_count desc;select segment ,count(distinct(product_code)) as product_count
    from dim_product
    group by segment 
    order by product_count desc; 

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>segment</th>
      <th>product\_count</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Notebook</td>
      <td>129</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Accessories</td>
      <td>116</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Peripherals</td>
      <td>84</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Desktop</td>
      <td>32</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Storage</td>
      <td>27</td>
    </tr>
    <tr>
      <th>5</th>
      <td>Networking</td>
      <td>9</td>
    </tr>
  </tbody>
</table>
</div>

## request 4:

Follow-up: Which segment had the most increase in unique products
in 2021 vs 2020? The final output contains these fields,
segment product\_count\_2020 product\_count\_2021 difference

    with
    Unique_Segment_Product_2020 as(
    select t2.segment,
    count(distinct(t1.product_code)) as unique_product_2020
    from fact_sales_monthly t1 join dim_product t2
    on t1.product_code = t2.product_code
    where fiscal_year = 2020
    group by t2.segment),
    Unique_Segment_Product_2021 as(
    select t2.segment,
    count(distinct(t1.product_code)) as unique_product_2021
    from fact_sales_monthly t1 join dim_product t2
    on t1.product_code = t2.product_code
    where fiscal_year = 2021
    group by t2.segment)
    select s1.segment , unique_product_2020 ,unique_product_2021,
    unique_product_2021 - unique_product_2020
    as difference
    from Unique_Segment_Product_2020 s1 join 
    Unique_Segment_Product_2021 s2 on
    s1.segment =s2.segment
    order by difference desc;

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>segment</th>
      <th>unique\_product\_2020</th>
      <th>unique\_product\_2021</th>
      <th>difference</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Accessories</td>
      <td>69</td>
      <td>103</td>
      <td>34</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Notebook</td>
      <td>92</td>
      <td>108</td>
      <td>16</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Peripherals</td>
      <td>59</td>
      <td>75</td>
      <td>16</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Desktop</td>
      <td>7</td>
      <td>22</td>
      <td>15</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Storage</td>
      <td>12</td>
      <td>17</td>
      <td>5</td>
    </tr>
    <tr>
      <th>5</th>
      <td>Networking</td>
      <td>6</td>
      <td>9</td>
      <td>3</td>
    </tr>
  </tbody>
</table>
</div>

## request 5:

Get the products that have the highest and lowest manufacturing
costs. The final output should contain these fields,
product\_code product manufacturing\_cost

```
select f.product_code , product ,manufacturing_cost
from fact_manufacturing_cost f join dim_product p
on f.product_code = p.product_code 
where f.manufacturing_cost in (
select max(manufacturing_cost) from fact_manufacturing_cost
union
select min(manufacturing_cost) from fact_manufacturing_cost)
order by manufacturing_cost desc;

```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>product\_code</th>
      <th>product</th>
      <th>manufacturing\_cost</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>A6120110206</td>
      <td>AQ HOME Allin1 Gen 2</td>
      <td>240.5364</td>
    </tr>
    <tr>
      <th>1</th>
      <td>A2118150101</td>
      <td>AQ Master wired x1 Ms</td>
      <td>0.8920</td>
    </tr>
  </tbody>
</table>
</div>

## request 6:

Generate a report which contains the top 5 customers who received an average high pre\_invoice\_discount\_pct for the fiscal year 2021 and in the Indian market. The final output contains these fields, customer\_code customer average\_discount\_percentage

    select c.customer_code ,c.customer,
    round(avg(d.pre_invoice_discount_pct),2) as avg_discount_pct	
    from dim_customer c join fact_pre_invoice_deductions d
    on c.customer_code = d.customer_code 
    where market = 'India'
    group by  c.customer_code
    order by avg_discount_pct desc limit 5;

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>customer\_code</th>
      <th>customer</th>
      <th>avg\_discount\_pct</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>90002009</td>
      <td>Flipkart</td>
      <td>0.30</td>
    </tr>
    <tr>
      <th>1</th>
      <td>90002003</td>
      <td>Ezone</td>
      <td>0.28</td>
    </tr>
    <tr>
      <th>2</th>
      <td>90002004</td>
      <td>Vijay Sales</td>
      <td>0.28</td>
    </tr>
    <tr>
      <th>3</th>
      <td>90002011</td>
      <td>Atliq Exclusive</td>
      <td>0.27</td>
    </tr>
    <tr>
      <th>4</th>
      <td>90002006</td>
      <td>Viveks</td>
      <td>0.27</td>
    </tr>
  </tbody>
</table>
</div>

## request 7:

Get the complete report of the Gross sales amount forthe customer “Atliq Exclusive” for each month .This analysis helps to get an idea of low and high-performing months and take strategic decisions. The final report contains these columns:Month Year Gross sales Amount

    select monthname(s.date) as month,
    year(s.date) year,s.fiscal_year	,
    round(sum(s.sold_quantity*g.gross_price)/1000000,2) as gross_amount
    from dim_customer c join fact_sales_monthly s
    on c.customer_code = s.customer_code join fact_gross_price g 
    on s.product_code = g.product_code
    where c.customer = 'Atliq Exclusive'
    group by year(s.date), monthname(s.date)
    order by year(s.date) asc , month(s.date) asc;

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>month</th>
      <th>year</th>
      <th>fiscal\_year</th>
      <th>gross\_amount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>September</td>
      <td>2019</td>
      <td>2020</td>
      <td>9.09</td>
    </tr>
    <tr>
      <th>1</th>
      <td>October</td>
      <td>2019</td>
      <td>2020</td>
      <td>10.38</td>
    </tr>
    <tr>
      <th>2</th>
      <td>November</td>
      <td>2019</td>
      <td>2020</td>
      <td>15.23</td>
    </tr>
    <tr>
      <th>3</th>
      <td>December</td>
      <td>2019</td>
      <td>2020</td>
      <td>9.76</td>
    </tr>
    <tr>
      <th>4</th>
      <td>January</td>
      <td>2020</td>
      <td>2020</td>
      <td>9.58</td>
    </tr>
    <tr>
      <th>5</th>
      <td>February</td>
      <td>2020</td>
      <td>2020</td>
      <td>8.08</td>
    </tr>
    <tr>
      <th>6</th>
      <td>March</td>
      <td>2020</td>
      <td>2020</td>
      <td>0.77</td>
    </tr>
    <tr>
      <th>7</th>
      <td>April</td>
      <td>2020</td>
      <td>2020</td>
      <td>0.80</td>
    </tr>
    <tr>
      <th>8</th>
      <td>May</td>
      <td>2020</td>
      <td>2020</td>
      <td>1.59</td>
    </tr>
    <tr>
      <th>9</th>
      <td>June</td>
      <td>2020</td>
      <td>2020</td>
      <td>3.43</td>
    </tr>
    <tr>
      <th>10</th>
      <td>July</td>
      <td>2020</td>
      <td>2020</td>
      <td>5.15</td>
    </tr>
    <tr>
      <th>11</th>
      <td>August</td>
      <td>2020</td>
      <td>2020</td>
      <td>5.64</td>
    </tr>
    <tr>
      <th>12</th>
      <td>September</td>
      <td>2020</td>
      <td>2021</td>
      <td>19.53</td>
    </tr>
    <tr>
      <th>13</th>
      <td>October</td>
      <td>2020</td>
      <td>2021</td>
      <td>21.02</td>
    </tr>
    <tr>
      <th>14</th>
      <td>November</td>
      <td>2020</td>
      <td>2021</td>
      <td>32.25</td>
    </tr>
    <tr>
      <th>15</th>
      <td>December</td>
      <td>2020</td>
      <td>2021</td>
      <td>20.41</td>
    </tr>
    <tr>
      <th>16</th>
      <td>January</td>
      <td>2021</td>
      <td>2021</td>
      <td>19.57</td>
    </tr>
    <tr>
      <th>17</th>
      <td>February</td>
      <td>2021</td>
      <td>2021</td>
      <td>15.99</td>
    </tr>
    <tr>
      <th>18</th>
      <td>March</td>
      <td>2021</td>
      <td>2021</td>
      <td>19.15</td>
    </tr>
    <tr>
      <th>19</th>
      <td>April</td>
      <td>2021</td>
      <td>2021</td>
      <td>11.48</td>
    </tr>
    <tr>
      <th>20</th>
      <td>May</td>
      <td>2021</td>
      <td>2021</td>
      <td>19.20</td>
    </tr>
    <tr>
      <th>21</th>
      <td>June</td>
      <td>2021</td>
      <td>2021</td>
      <td>15.46</td>
    </tr>
    <tr>
      <th>22</th>
      <td>July</td>
      <td>2021</td>
      <td>2021</td>
      <td>19.04</td>
    </tr>
    <tr>
      <th>23</th>
      <td>August</td>
      <td>2021</td>
      <td>2021</td>
      <td>11.32</td>
    </tr>
  </tbody>
</table>
</div>

## request 8:

In which quarter of 2020, got the maximum total\_sold\_quantity?The final output contains these fields sorted by the total\_sold\_quantity,Quarter total\_sold\_quantity

    with base_query as (
    select *,date_add(date,interval  4 month) as formatted_date  from fact_sales_monthly)
    select year(formatted_date) as fiscal_year,
    quarter(formatted_date) as quarter_  ,
    sum(sold_quantity) as total_sold_quantity
    from base_query group by year(formatted_date) , quarter(formatted_date);

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>fiscal\_year</th>
      <th>quarter\_</th>
      <th>total\_sold\_quantity</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2020</td>
      <td>1</td>
      <td>7005619.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2020</td>
      <td>2</td>
      <td>6649642.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2020</td>
      <td>3</td>
      <td>2075087.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2020</td>
      <td>4</td>
      <td>5042541.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2021</td>
      <td>1</td>
      <td>14476194.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>2021</td>
      <td>2</td>
      <td>13812652.0</td>
    </tr>
    <tr>
      <th>6</th>
      <td>2021</td>
      <td>3</td>
      <td>10984739.0</td>
    </tr>
    <tr>
      <th>7</th>
      <td>2021</td>
      <td>4</td>
      <td>10890997.0</td>
    </tr>
  </tbody>
</table>
</div>

## request 9:

Which channel helped to bring more gross sales in the fiscal year 2021 and the percentage of contribution? The final output contains these fields,channel gross\_sales\_mln percentage

    with base_query as(
    select s.fiscal_year,c.channel ,s.sold_quantity , g.gross_price ,
    (s.sold_quantity*g.gross_price) as gross_amount
    from fact_sales_monthly s join fact_gross_price g 
    on s.product_code = g.product_code join dim_customer c on
    s.customer_code = c.customer_code where s.fiscal_year = 2021)

    select channel,total_gross_amount,
    round((total_gross_amount/sum(total_gross_amount) over())*100,2) as pct from 
    (select channel ,sum(gross_amount) as total_gross_amount from base_query
    group by channel) t order by pct desc limit 1;

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>channel</th>
      <th>total\_gross\_amount</th>
      <th>pct</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Retailer</td>
      <td>1.924170e+09</td>
      <td>73.22</td>
    </tr>
  </tbody>
</table>
</div>

## request 10:

Get the Top 3 products in each division that have a high total\_sold\_quantity in the fiscal\_year 2021? The final output contains these fields,division product\_code

    with base_query as
    (select s.fiscal_year,p.division,s.product_code,p.product,s.sold_quantity,
    rank() over(partition by p.division order by sold_quantity desc ) as product_rank_division
    from fact_sales_monthly s join dim_product p on s.product_code = p.product_code
    where fiscal_year =2021 group by p.division ,p.product_code)
    select * from base_query where product_rank_division < 4

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>fiscal\_year</th>
      <th>division</th>
      <th>product\_code</th>
      <th>product</th>
      <th>sold\_quantity</th>
      <th>product\_rank\_division</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2021</td>
      <td>N \& S</td>
      <td>A6818160202</td>
      <td>AQ Pen Drive DRC</td>
      <td>1331</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2021</td>
      <td>N \& S</td>
      <td>A6319160202</td>
      <td>AQ Neuer SSD</td>
      <td>944</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2021</td>
      <td>N \& S</td>
      <td>A6819160203</td>
      <td>AQ Pen Drive DRC</td>
      <td>940</td>
      <td>3</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2021</td>
      <td>P \& A</td>
      <td>A2319150302</td>
      <td>AQ Gamers Ms</td>
      <td>1038</td>
      <td>1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2021</td>
      <td>P \& A</td>
      <td>A2721150704</td>
      <td>AQ Trigger Ms</td>
      <td>1034</td>
      <td>2</td>
    </tr>
    <tr>
      <th>5</th>
      <td>2021</td>
      <td>P \& A</td>
      <td>A2520150501</td>
      <td>AQ Maxima Ms</td>
      <td>1000</td>
      <td>3</td>
    </tr>
    <tr>
      <th>6</th>
      <td>2021</td>
      <td>PC</td>
      <td>A4520110504</td>
      <td>AQ Gen X</td>
      <td>39</td>
      <td>1</td>
    </tr>
    <tr>
      <th>7</th>
      <td>2021</td>
      <td>PC</td>
      <td>A4218110207</td>
      <td>AQ Digit</td>
      <td>38</td>
      <td>2</td>
    </tr>
    <tr>
      <th>8</th>
      <td>2021</td>
      <td>PC</td>
      <td>A4218110205</td>
      <td>AQ Digit</td>
      <td>37</td>
      <td>3</td>
    </tr>
  </tbody>
</table>
</div>



Resume Project challenge of providing insight on Consumer Goods Domain

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# Introduction

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply ML algorithms to produce AI systems. These systems generate insights that analysts and business users can translate into tangible business value. Here I will discuss different non-tool skills which will help one succeed in this career path easily.

![](https://miro.medium.com/v2/resize:fit:700/0*zvDPqq6aMtzCbMar)Photo by [Lukas Blazek](https://unsplash.com/@goumbik?utm_source=medium\&utm_medium=referral) on [Unsplash](https://unsplash.com/?utm_source=medium\&utm_medium=referral)

## 1. Patience

This is the most important skill required to be a good data science professional. Most of the time people get impatient while learning difficult concepts, and tools and when they do not get immediate results. Data science is more about the journey than the destination, so enjoy this journey as much as possible.

## 2. Avoide Comparision

Try to avoid comparing your progress with others because your destination may be the same but the journey is different, rather compare your progress with yourself and how much you improved.

## 3. Procrastination

Procrastination is the act of delaying or postponing a task or set of tasks. This habit creates lots of problems during preparation because it piles up tasks and increases the time duration required to complete, eventually it decreases the motivation level. So avoid it as soon as possible.

## 4. Time Management

Manage your time properly because this is the most valuable asset. Make a proper schedule. Be flexible in your schedule. Try to allot time to every other thing be it your social obligation, family time, fitness and job etc.

## 5. Neophobia

It stands for fear of trying out new things. During the data science journey, one has to learn a lot of different skills. So never fear to try out new tools if required. The most important point here is to start. Eventually, you will feel comfortable once you dedicate the minimum required hours.

## 6. Atychiphobia

This stands for an intense fear of failure. It may cause you to put off or avoid any activity or scenario that has the potential for an unsuccessful outcome like mock interviews, mock tests or initial real interviews where there is a high chance of rejection. But remember one thing every failure gives you some amount of experience. So do it even if you don’t feel comfortable.

## 7. Feeling Under Prepared

You may never get to a moment where you feel completely ready. There will be selective preparedness and calculated unknowns, but never all the way ready, so start anyway.

## 8. Ignore The Noise

You might come across these lines, There will be no job in the future AI will replace everything. But believe me, AI will not take your job, a person with knowledge of AI will take your job. So prepare to become that person who can create or use AI.

## 9. Avoide Learning Too Many Things simultaneously

Try to learn technology one by one with a proper roadmap.

## 10. Trying To Master One Technolgy

The Data Science curriculum is huge, so never dedicated your entire time to mastering one technology. It is very important to know what not to learn rather than what to learn.

## 11. Communication Skill

By far the most important non-tech skill one should have. You must have very good communication skills. Because it is the most useful skill which helps you starting from your interviews to day-to-day work in the data science industry. A lot of time you need to engage with stakeholders, clients, colleagues, and data engineers. This helps you to ask the right questions, deliver your thought and many more.

## 12. Problem-Solving Acumen

How you react to a problem, How structured your thinking is, and How much analytical skill you have to solve the problem. These things matter most

## 13. Story Telling

Presenting your work in terms of story is one of the great skills to have. It helps to engage your audience with your idea.

## 14. Project Management Skills

Ability to work with a team and collaborate with others, knowing Agile Methodology principles always helps.

## 15. Domain Knowledge

This is the most in-demand skill because it has no substitution, It comes with experience and this skill is not for freshers.

## 16. Corporate Fit

Finally, last but not least you must have good interpersonal skills or soft skills which make you a corporate fit.


Top Non-Tool Skills Required For Data Science Aspirants 🚀

If you like the content please leave a clap


Habits of highly productive web developers ⌨️


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Etiam facilisis lacus nec pretium lobortis. Praesent dapibus justo non efficitur efficitur. Nullam viverra justo arcu, eget egestas tortor pretium id. Sed imperdiet mattis eleifend. Vivamus suscipit et neque imperdiet venenatis. In malesuada sed urna eget vehicula. Donec fermentum tortor sit amet nisl elementum fringilla. Pellentesque dapibus suscipit faucibus. Nullam malesuada sed urna quis rutrum. Donec facilisis lorem id maximus mattis. Vestibulum quis elit magna. Vestibulum accumsan blandit consequat. Phasellus quis posuere quam.

Vestibulum ullamcorper risus auctor eleifend consequat. Vivamus mollis in tellus ac ullamcorper. Vestibulum sit amet bibendum ipsum, vitae rutrum ex. Nullam cursus, urna et dapibus aliquam, urna leo euismod metus, eu luctus justo mi eget mauris. Proin felis leo, volutpat et purus in, lacinia luctus eros. Pellentesque lobortis massa scelerisque lorem ullamcorper, sit amet elementum nulla scelerisque. In volutpat efficitur nulla, aliquam ornare lectus ultricies ac. Mauris sagittis ornare dictum. Nulla vel felis ut purus fermentum pretium. Sed id lectus ac diam aliquet venenatis. Etiam ac auctor enim. Nunc velit mauris, viverra vel orci ut, egestas rhoncus diam. Morbi scelerisque nibh tellus, vel varius urna malesuada sed. Etiam ultricies sem consequat, posuere urna non, maximus ex. Mauris gravida diam sed augue condimentum pulvinar vel ac dui. Integer vel convallis justo.

Nam rutrum magna sed pellentesque lobortis. Etiam quam mauris, iaculis eget ex ac, rutrum scelerisque nisl. Cras finibus dictum ex sed tincidunt. Morbi facilisis neque porta, blandit mauris quis, pharetra odio. Aliquam dictum quam quis elit auctor, at vestibulum ex pulvinar. Quisque lobortis a lectus quis faucibus. Nulla vitae pellentesque nibh, et fringilla erat. Praesent placerat ac est at tincidunt. Praesent ultricies a ex at ultrices. Etiam sed tincidunt elit. Nulla sagittis neque neque, ultrices dignissim sapien pellentesque faucibus. Donec tempor orci sed consectetur dictum. Ut viverra ut enim ac semper. Integer lacinia sem in arcu tempor faucibus eget non urna. Praesent vel nunc eu libero aliquet interdum non vitae elit. Maecenas pharetra ipsum dolor, et iaculis elit ornare ac.

Aenean scelerisque ullamcorper est aliquet blandit. Donec ac tellus enim. Vivamus quis leo mattis, varius arcu at, convallis diam. Donec ac leo at nunc viverra molestie ac viverra nisi. Proin interdum at turpis at varius. Nunc sit amet ex suscipit, convallis ligula eu, pretium turpis. Sed ultricies neque vel mi malesuada, et mollis risus lobortis. Sed condimentum venenatis mauris, id elementum dolor gravida ac. Sed sodales tempus neque, quis iaculis arcu tincidunt ut. Donec vitae faucibus dui. In hac habitasse platea dictumst. Donec erat ex, ullamcorper a massa a, porttitor porta ligula.


How to Structure and Organize a Next.js Project 🗂️


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Etiam facilisis lacus nec pretium lobortis. Praesent dapibus justo non efficitur efficitur. Nullam viverra justo arcu, eget egestas tortor pretium id. Sed imperdiet mattis eleifend. Vivamus suscipit et neque imperdiet venenatis. In malesuada sed urna eget vehicula. Donec fermentum tortor sit amet nisl elementum fringilla. Pellentesque dapibus suscipit faucibus. Nullam malesuada sed urna quis rutrum. Donec facilisis lorem id maximus mattis. Vestibulum quis elit magna. Vestibulum accumsan blandit consequat. Phasellus quis posuere quam.

> “Everybody should learn to program a computer, because it teaches you how to think.”

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Nam rutrum magna sed pellentesque lobortis. Etiam quam mauris, iaculis eget ex ac, rutrum scelerisque nisl. Cras finibus dictum ex sed tincidunt. Morbi facilisis neque porta, blandit mauris quis, pharetra odio. Aliquam dictum quam quis elit auctor, at vestibulum ex pulvinar. Quisque lobortis a lectus quis faucibus. Nulla vitae pellentesque nibh, et fringilla erat. Praesent placerat ac est at tincidunt. Praesent ultricies a ex at ultrices. Etiam sed tincidunt elit. Nulla sagittis neque neque, ultrices dignissim sapien pellentesque faucibus. Donec tempor orci sed consectetur dictum. Ut viverra ut enim ac semper. Integer lacinia sem in arcu tempor faucibus eget non urna. Praesent vel nunc eu libero aliquet interdum non vitae elit. Maecenas pharetra ipsum dolor, et iaculis elit ornare ac.

Aenean scelerisque ullamcorper est aliquet blandit. Donec ac tellus enim. Vivamus quis leo mattis, varius arcu at, convallis diam. Donec ac leo at nunc viverra molestie ac viverra nisi. Proin interdum at turpis at varius. Nunc sit amet ex suscipit, convallis ligula eu, pretium turpis. Sed ultricies neque vel mi malesuada, et mollis risus lobortis. Sed condimentum venenatis mauris, id elementum dolor gravida ac. Sed sodales tempus neque, quis iaculis arcu tincidunt ut. Donec vitae faucibus dui. In hac habitasse platea dictumst. Donec erat ex, ullamcorper a massa a, porttitor porta ligula.


NFTs are so hot right now


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## Heading 2

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### Heading 3

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```javascript
{
  page.markdown_content && (
    <Markdown
      options={{ forceBlock: true, overrides: { pre: HighlightedPreBlock } }}
      className="sb-markdown max-w-screen-md mx-auto"
      data-sb-field-path="markdown_content"
    >
      {page.markdown_content}
    </Markdown>
  );
}
```

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Nam rutrum magna sed pellentesque lobortis. Etiam quam mauris, iaculis eget ex ac, rutrum scelerisque nisl. Cras finibus dictum ex sed tincidunt. Morbi facilisis neque porta, blandit mauris quis, pharetra odio. Aliquam dictum quam quis elit auctor, at vestibulum ex pulvinar. Quisque lobortis a lectus quis faucibus. Nulla vitae pellentesque nibh, et fringilla erat. Praesent placerat ac est at tincidunt. Praesent ultricies a ex at ultrices. Etiam sed tincidunt elit. Nulla sagittis neque neque, ultrices dignissim sapien pellentesque faucibus. Donec tempor orci sed consectetur dictum. Ut viverra ut enim ac semper. Integer lacinia sem in arcu tempor faucibus eget non urna. Praesent vel nunc eu libero aliquet interdum non vitae elit. Maecenas pharetra ipsum dolor, et iaculis elit ornare ac.

Aenean scelerisque ullamcorper est aliquet blandit. Donec ac tellus enim. Vivamus quis leo mattis, varius arcu at, convallis diam. Donec ac leo at nunc viverra molestie ac viverra nisi. Proin interdum at turpis at varius. Nunc sit amet ex suscipit, convallis ligula eu, pretium turpis. Sed ultricies neque vel mi malesuada, et mollis risus lobortis. Sed condimentum venenatis mauris, id elementum dolor gravida ac. Sed sodales tempus neque, quis iaculis arcu tincidunt ut. Donec vitae faucibus dui. In hac habitasse platea dictumst. Donec erat ex, ullamcorper a massa a, porttitor porta ligula.


Sharing my wisdom with the world 🌎

About Dataset
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The Uncleaned Laptop Price dataset is a collection of laptop product listings scraped from an online e-commerce website. The dataset includes information about various laptop models, such as their brand, screen size, processor, memory, storage capacity, operating system, and price. However, the dataset is uncleaned, meaning that it contains missing values, inconsistent formatting, and other errors that need to be addressed before the data can be used for analysis or modeling.

The dataset contains both categorical and numerical variables, with the majority of variables being categorical, including brand, model name, screen resolution, processor type, and operating system. Some numerical variables include screen size, memory, and storage capacity. The target variable in the dataset is the price, which is a continuous variable.

The dataset contains over 1,300 laptop listings, making it a reasonably sized dataset for analysis and modeling. The dataset may be useful for machine learning projects related to predicting the price of a laptop based on its specifications. However, before using the dataset, it would be necessary to clean and preprocess the data to address the inconsistencies and missing values.

[Dataset Links](https://www.kaggle.com/datasets/ehtishamsadiq/uncleaned-laptop-price-dataset)


I am going to clean this dataset with mysql queries . Although it is easy to clean dataset with help of python but as most cases in real world data lies within database so it is better to do initial level data level data cleaning  through sql . Here I have used lots of string functions ,DDL and DML commands of mysql

###### steps to do
1. first download dataset from above link
2. create a database name project
3. import dataset to mysql server through import wizard and name table laptop

#### Initial look of data
```sql
    Select * from laptop
```






<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }


</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>index</th>
      <th>Unnamed: 0</th>
      <th>Company</th>
      <th>TypeName</th>
      <th>Inches</th>
      <th>ScreenResolution</th>
      <th>Cpu</th>
      <th>Ram</th>
      <th>Memory</th>
      <th>Gpu</th>
      <th>OpSys</th>
      <th>Weight</th>
      <th>Price</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>Apple</td>
      <td>Ultrabook</td>
      <td>13.3</td>
      <td>IPS Panel Retina Display 2560x1600</td>
      <td>Intel Core i5 2.3GHz</td>
      <td>8GB</td>
      <td>128GB SSD</td>
      <td>Intel Iris Plus Graphics 640</td>
      <td>macOS</td>
      <td>1.37kg</td>
      <td>71378.6832</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>Apple</td>
      <td>Ultrabook</td>
      <td>13.3</td>
      <td>1440x900</td>
      <td>Intel Core i5 1.8GHz</td>
      <td>8GB</td>
      <td>128GB Flash Storage</td>
      <td>Intel HD Graphics 6000</td>
      <td>macOS</td>
      <td>1.34kg</td>
      <td>47895.5232</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>2</td>
      <td>HP</td>
      <td>Notebook</td>
      <td>15.6</td>
      <td>Full HD 1920x1080</td>
      <td>Intel Core i5 7200U 2.5GHz</td>
      <td>8GB</td>
      <td>256GB SSD</td>
      <td>Intel HD Graphics 620</td>
      <td>No OS</td>
      <td>1.86kg</td>
      <td>30636.0000</td>
    </tr>
  </tbody>
</table>
</div>



#### count of rows in   dataset
```sql
    select count(*) from laptop
```






<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }


</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>count(*)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1272</td>
    </tr>
  </tbody>
</table>
</div>



#### Let's create a backup data
```sql
    create table laptop_backup like	 laptop;
    insert into laptop_backup select * from laptop;

```

#### count of rows in  backup dataset
```sql
    select count(*) from laptop_backup
```

#### check for space occupied
```sql
                    select DATA_LENGTH/1024 from information_schema.tables 
                    where table_schema = 'project'
                    and table_name = 'laptop';
```






<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }


</style>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>DATA_LENGTH/1024</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>256.0</td>
    </tr>
  </tbody>
</table>
</div>



#### drop unnecessary column
```sql
     alter table laptop drop column `Unnamed: 0`;
     Select * from laptop;
```

#### check for null rows
```sql
        select `index` from laptop
        where Company is null and TypeName is null and Inches is null 
        and ScreenResolution is null and `Cpu` is null and Ram is null 
        and `Memory` is null and Gpu is null and OpSys is null and
        Weight is null and Price is null;
```

#### delete all the null row
```sql
        delete from laptop where `index` in (
        select `index` from laptop
        where Company is null and TypeName is null and Inches is null 
        and ScreenResolution is null and `Cpu` is null and Ram is null 
        and `Memory` is null and Gpu is null and OpSys is null and
        Weight is null and Price is null)
```

#### check for duplicate row
``` sql

    select Company ,TypeName ,Inches,ScreenResolution ,`Cpu`,Ram,`Memory`,
    Gpu,OpSys,Weight,price,count(*) from laptop group by Company ,TypeName ,Inches,
    ScreenResolution ,`Cpu`,Ram,`Memory`,Gpu,OpSys,Weight,price having  count(*)>1;

```

#### delete duplicate row
``` sql
    with base_query as(select min(`index`)from laptopdata group by Company ,
                        TypeName ,Inches,ScreenResolution ,
                        `Cpu`,Ram,`Memory`,Gpu,OpSys,Weight,price)
    delete from laptop where `index` not in (select * from base_query);
```


```python
pd.read_sql_query("Select count(*) from laptop",conn)
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }


</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>count(*)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1243</td>
    </tr>
  </tbody>
</table>
</div>



## Inches Column
##### changing Inches to datatpe to decimal
```sql
    alter table laptop modify column  Inches Decimal(10,1);
```

## Ram Column
##### removing GB
```sql
    update laptop set Ram = replace(Ram,'GB','') ;
```

#### Changing data type of Ram column to integer
```sql
    alter table laptop modify column Ram INTEGER;
```

## Weight Column
#### removing kg
```sql
   update laptop set Weight = replace(Weight,'kg','') ;
```

### changing data type of Weight column to decimal
```sql
       alter table laptop modify column Weight Decimal(10,2);
```

### delete record where weight is 0
```sql
       delete from laptop where Weight = 0;
```

### Price
###### rounding of price
```sql
    update laptop set Price = round(Price);
```

##### changing price column datatype to integer
```sql
    alter table laptop modify column Price INTEGER;
```

### OpSYS
###### changing OpSYS values 
###### there are values like windows 10,windows11,windows 7 ....,we are going to convert all to windows similarly to other
``` sql
    update laptop set OpSYS = 
case 
	when OpSys like '%mac%' then 'Mac'
    when OpSys like '%windows%' then 'Windows'
    when OpSys like '%Linux%' then 'Linux'
    when OpSys like '%chrome%' then 'Chrome'
    when OpSys like '%android%' then 'Android'
    when OpSys = 'No OS' then 'N/A'
    else 'Other'
end;
```

## Gpu
##### strategy is to extract Brand name from Gpu column and drop the Gpu column
```sql
alter table laptop 
add column gpu_brand varchar(255) after Gpu;


update laptop set gpu_brand = substring_index(Gpu ,' ',1);


alter table laptop drop column Gpu;

```

## Cpu
##### strategy is to make make three new column name cpu_brand,cpu_speed,cpu_name from cpu and finaly drop cpu column . also in cpu speed column I am going to remove GHz and convert that column to decimal
```sql
    alter table laptop
    add column cpu_brand varchar(255) after `Cpu`,
    add column cpu_name varchar(255) after cpu_brand,
    add column cpu_speed decimal(10,1) after cpu_name; 
    
    update laptop set cpu_brand = substring_index(`Cpu` ," ",1);
    
    update laptop 
    set cpu_speed = cast( replace(substring_index(`Cpu`,' ',-1),'GHz',"")  as decimal(10,1) );
    
    update laptop 
    set cpu_name = replace(replace(`Cpu`,cpu_brand,''),substring_index(`Cpu`,' ',-1),'');
    
    update laptop
    set cpu_name = substring_index(trim(cpu_name)," ",2);
    
    alter table laptop drop column `Cpu`;

```


## Screen Resolution
#### strategy is to add four column name resolution_width , resolution_height ,is_touchscreen,is_ips_panel and drop ScreenResolution 
```sql
    alter table laptop
    add column resolution_width integer after ScreenResolution,
    add column resolution_height integer after resolution_width,
    add column is_touchscreen integer after resolution_height,
    add column is_ips_panel integer after is_touchscreen;
    
    update laptop
    set resolution_width = substring_index(substring_index(ScreenResolution," ",-1),"x",1),
    resolution_height = substring_index(substring_index(ScreenResolution," ",-1),"x",-1),
    is_touchscreen  = ScreenResolution like '%Touch%',
    is_ips_panel  = ScreenResolution like '%IPS%';

    alter table laptop drop column ScreenResolution;

```


## Memory
##### strategy is to make three new column name memory_type , primary_storage, secondary storage also all the memoryis going to be in Gb
```sql
    alter table laptop 
    add column memory_type varchar(255) after Memory,
    add column primary_storage integer after memory_type,
    add column secondary_storage integer after primary_storage;
    
    
    update laptop
    set memory_type =
    case
        when Memory like '%hybrid%' then 'Hybrid'
        when Memory like '%SSD%' and Memory like '%HDD%' then 'Hybrid'
        when Memory like '%Flash Storage%' and Memory like '%HDD%' then 'Hybrid'
        when Memory like '%SSD%' then 'SSD'
        when Memory like '%HDD%' then 'HDD'
        when Memory like '%Flash Storage%' then 'Flash Storage'
        else null
    end;
    
    update laptop set
    primary_storage = regexp_substr(substring_index(Memory,'+',1),'[0-9]+');
    
    update laptop set
    secondary_storage = 
    case 
		when Memory like '%+%' then regexp_substr(substring_index(Memory,'+',-1),'[0-9]+')
        else 0
    end;
    
    
    
    update laptop set 
     primary_storage = 
     case 
         when primary_storage <=3 then primary_storage*1024
         else primary_storage 
     end,
     
     secondary_storage = 
     case 
          when secondary_storage <=3 then secondary_storage*1024
          else secondary_storage
     end;
     
     
     alter table laptop drop column Memory;

```



```python
pd.read_sql_query("Select * from laptop",conn).head(3)
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }


</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>index</th>
      <th>Company</th>
      <th>TypeName</th>
      <th>Inches</th>
      <th>is_ips_panel</th>
      <th>is_touchscreen</th>
      <th>resolution_width</th>
      <th>resolution_height</th>
      <th>cpu_brand</th>
      <th>cpu_name</th>
      <th>cpu_speed</th>
      <th>Ram</th>
      <th>memory_type</th>
      <th>primary_storage</th>
      <th>secondary_storage</th>
      <th>gpu_brand</th>
      <th>OpSys</th>
      <th>Weight</th>
      <th>Price</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Apple</td>
      <td>Ultrabook</td>
      <td>13.3</td>
      <td>1</td>
      <td>0</td>
      <td>2560</td>
      <td>1600</td>
      <td>Intel</td>
      <td>Core i5</td>
      <td>2.3</td>
      <td>8</td>
      <td>SSD</td>
      <td>128.0</td>
      <td>0</td>
      <td>Intel</td>
      <td>Mac</td>
      <td>1.37</td>
      <td>71379</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Apple</td>
      <td>Ultrabook</td>
      <td>13.3</td>
      <td>0</td>
      <td>0</td>
      <td>1440</td>
      <td>900</td>
      <td>Intel</td>
      <td>Core i5</td>
      <td>1.8</td>
      <td>8</td>
      <td>Flash Storage</td>
      <td>128.0</td>
      <td>0</td>
      <td>Intel</td>
      <td>Mac</td>
      <td>1.34</td>
      <td>47896</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>HP</td>
      <td>Notebook</td>
      <td>15.6</td>
      <td>0</td>
      <td>0</td>
      <td>1920</td>
      <td>1080</td>
      <td>Intel</td>
      <td>Core i5</td>
      <td>2.5</td>
      <td>8</td>
      <td>SSD</td>
      <td>256.0</td>
      <td>0</td>
      <td>Intel</td>
      <td>N/A</td>
      <td>1.86</td>
      <td>30636</td>
    </tr>
  </tbody>
</table>
</div>



JAMstack the future of web


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## About Coding

> “Coding is the language of the future, and every girl should learn it. As I've learned from watching girls grow and learn in our classrooms, coding is fun, collaborative and creative.”

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## Productivity

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